基于知识自动获取的无指导译文消歧方法研究
|
摘要
这是个互联网的世界,使用高效的搜索引擎在互联网获取信息已经成为当代人们获取信息的最重要手段。在日益国际化的信息中,不同种语言之间的理解与处理一直存在着难以逾越的鸿沟,这就形成了人们对机器翻译、跨语言信息检索与处理的迫切需求。目前对此研究仍有诸多难题亟需解决,其主要之一就是,如何为源语言多义词选择语义正确对应的目标语译文词汇的问题,称之为译文消歧。译文消歧及与之相似的在单语范畴内的词义消歧一直是自然语言处理领域基础研究课题,它也是自然语言处理技术的重点和难点之一。
针对译文消歧及词义消歧的现状,通过对各类无指导消歧方法的比较分析,本文认为,目前无指导译文消歧的关键问题是消歧知识的自动获取与利用、克服数据稀疏及双语语义词典建设。因此,本文没有在机器学习算法、消歧特征选择等问题上做过多的研究与探讨,而是充分关注与挖掘无指导译文消歧方法中最核心的内容——知识获取,利用这些消歧知识来完成无指导译文消歧任务,同时克服数据稀疏问题。由此思想出发,本文提出了一系列逐步递进的无指导译文消歧知识获取以及消歧的创新方法,这些创新方法均利用了国际标准语义评测语料进行评测与对比分析,并均超过了以往可比较的最好无指导系统。最后,本文还进行了另一个关键问题的研究,即双语语义词典的自动构建。
本文具体研究内容包括以下几个方面:
1.自动获取带标记目标语语料,并直接形成译文消歧模型,提出利用该模型进行译文消歧的方法。在此基础上,提出了等价伪译词概念以及等价伪译词的构造方法,并以此实现无指导的译文消歧。最后在国际语义评测数据集Senseval-2 ELS上进行了实验与比较;
2.通过对双语语料库间接关联的观察,提出利用双语词汇间接关联度的完全无指导译文消歧方法。在计算间接关联度的过程中充分利用了Web资源,设计了Web的词汇间接关联度(Web_IA)的计算方法,在消歧过程中利用了三种不同的决策方法进行决策。随后,针对基于间接关联方法的不足,本文将整个Web视为语义词典,直接利用Web进行双语词汇语义相关度(WBR)的定义分析以及计算。经过对WBR方法在一个经改造处理的标准语义相关度测试集上的比较实验,证明该方法可行后,设计了基于WBR的完全无指导译文消歧方法,并在同一个国际标准语义评测数据集Semeval2007上的task5与基于Web_IA方法做了详细的对比实验;
3.通过对歧义词同义词集合内词汇语句序列的观察,提出了一种基于Ngram语言模型以及Web挖掘的无指导译文消歧方法。该方法认为歧义词不同词义所对应的N-gram语言模型模式不同,且利用的是语言模型知识而非语义知识。随后在同一个标准集上的对比评测表明,该方法取得了极为优异的性能。性能超过了该任务可比较最好无指导系统TorMD12.8%(Pmar值),最后,还进行了基于语言模型的方法与基于语义模型的方法的详细比较及性能上限的讨论;
4.研究了利用WordNet、HowNet以及大规模双语平行语料库自动生成面向译文消歧的的双语词典的方法。该方法充分利用大规模平行语料库内丰富的词汇对齐知识以及各项统计信息,利用WordNet及HowNet语义资源的相似度计算,形成了一部同时带有双语语义信息及语料库统计信息的词典。
综上所述,本文基本上给出了面向知识自动获取的无指导译文消歧的一整套解决方案,特别是其中基于Web的各种方法,为自然语言处理中的难题之一——译文/词义消歧,在基于Web搜索的研究思路上进行了初步探索。
Internet is the king of the world at present. To most people nowadays, it is the most important mean to acquire information from Internet by an efficient search engine. There is an insurmountable barrier between understanding different kind of languages in this more and more international information so that the most urgent problem for us is to research machine translation (MT) and cross-language information retrieval (CLIR). There are many hard problems have not been resolved on the research yet, one is how to select right target language translation while facing an ambiguity source language word, which is so-called the problem of word translation disambiguation (WTD). WTD and its similar task - word sense disambiguation (WSD) in mono-lingual category are important and hard in the research of nature language processing (NLP) and are always the basis of it.
Facing the current situation, through the comparision and analysis on all kinds of unsupervised disambiguation methods, this thesis considers that the key problem of unsupervised WTD/WSD is in three fields: knowledge acquisition, data sparseness and the construction of biligual semantic resourse. So, aiming at the study of knowledege acquisition and conquer data sparseness which are the core problems in unsupervised WTD/WSD, this thesis introduces a series of creative methods on unsupervised WTD knowledge acquisition. All of the methods are evaluated on the international golden set of semantic evalution and are all superior to the best comparable systems and get the state of arts result. Another key problem in WTD - bilingual semantic dictionary auto-construction is also studied.
In detail, this thesis is arranged as following:
1. Automatic sense-tagged corpus acquisition, forms the disambiguation classifier directly and introduces the classifier to WTD are all studied. On the basis of that, the thesis introduces the concept of Equivalent PseudoTranslation (EPT) and the WTD method based on it. Finally, it is compared with other unsupervised systems on Senseval-2 English Lexical Sample task.
2. A fully unsupervised WTD method based on the indirect association(IA) between bilingual words and Web mining is introduced through our investigation in biligual parallel corpus. Four methods of computing IA on Web are designed and three different kind of decision strategics are used during the disambiguation process. Futhermore, the thesis considers the Web as a special semantic lexicon so that the relatedness between bilingual words (WBR) could be defined and computed directly by using Page Counts which Web search engine returned. After testing WBR on a revised golden data set and proving its feasible, a fully unsupervised method is designed for WTD. Both the IA method and the WBR method of WTD are tested on Semeval 2007 task 5- Multilingual Chinese-English Lexical Sample task and made comparison on it.
3. An unsupervised WTD method based on the N-gram language model(LM) and Web mining is introduced after the observation of varies series of synonyms in different sentences. On the basis of the supposition that―different sense, different N-gram pattern‖, the model make disambiguation by using LM knowledge not by semantic knowledge. After testing on the same golden set for comparison with other systems, it shows that the performance of the proposed model is excellent and out-performs all other comparable unsupervised systems. Detail comparison and combining upperbound between LM based model and semantic based model is discussed either
4. A methods that can generate a WTD application-oriented bilingual semantic dictionary automatically from WordNet, HowNet and an large-scale blingual parallel corpus is studied. The method mainly uses similarity of WordNet and HowNet between words to filter statistic noise data between the processing of word alignment. Finally, it forms a bilingual semantic dictionary which has imformation of these three resources: WordNet, HowNet and bilingual parallel corpus.
In brief, basiclly, a complete set of knowledge acquisition-oriented WTD solution methods has been established, especially the Web-based methods which explored the hard problem in NLP - WTD/WSD on the Web search way.
针对译文消歧及词义消歧的现状,通过对各类无指导消歧方法的比较分析,本文认为,目前无指导译文消歧的关键问题是消歧知识的自动获取与利用、克服数据稀疏及双语语义词典建设。因此,本文没有在机器学习算法、消歧特征选择等问题上做过多的研究与探讨,而是充分关注与挖掘无指导译文消歧方法中最核心的内容——知识获取,利用这些消歧知识来完成无指导译文消歧任务,同时克服数据稀疏问题。由此思想出发,本文提出了一系列逐步递进的无指导译文消歧知识获取以及消歧的创新方法,这些创新方法均利用了国际标准语义评测语料进行评测与对比分析,并均超过了以往可比较的最好无指导系统。最后,本文还进行了另一个关键问题的研究,即双语语义词典的自动构建。
本文具体研究内容包括以下几个方面:
1.自动获取带标记目标语语料,并直接形成译文消歧模型,提出利用该模型进行译文消歧的方法。在此基础上,提出了等价伪译词概念以及等价伪译词的构造方法,并以此实现无指导的译文消歧。最后在国际语义评测数据集Senseval-2 ELS上进行了实验与比较;
2.通过对双语语料库间接关联的观察,提出利用双语词汇间接关联度的完全无指导译文消歧方法。在计算间接关联度的过程中充分利用了Web资源,设计了Web的词汇间接关联度(Web_IA)的计算方法,在消歧过程中利用了三种不同的决策方法进行决策。随后,针对基于间接关联方法的不足,本文将整个Web视为语义词典,直接利用Web进行双语词汇语义相关度(WBR)的定义分析以及计算。经过对WBR方法在一个经改造处理的标准语义相关度测试集上的比较实验,证明该方法可行后,设计了基于WBR的完全无指导译文消歧方法,并在同一个国际标准语义评测数据集Semeval2007上的task5与基于Web_IA方法做了详细的对比实验;
3.通过对歧义词同义词集合内词汇语句序列的观察,提出了一种基于Ngram语言模型以及Web挖掘的无指导译文消歧方法。该方法认为歧义词不同词义所对应的N-gram语言模型模式不同,且利用的是语言模型知识而非语义知识。随后在同一个标准集上的对比评测表明,该方法取得了极为优异的性能。性能超过了该任务可比较最好无指导系统TorMD12.8%(Pmar值),最后,还进行了基于语言模型的方法与基于语义模型的方法的详细比较及性能上限的讨论;
4.研究了利用WordNet、HowNet以及大规模双语平行语料库自动生成面向译文消歧的的双语词典的方法。该方法充分利用大规模平行语料库内丰富的词汇对齐知识以及各项统计信息,利用WordNet及HowNet语义资源的相似度计算,形成了一部同时带有双语语义信息及语料库统计信息的词典。
综上所述,本文基本上给出了面向知识自动获取的无指导译文消歧的一整套解决方案,特别是其中基于Web的各种方法,为自然语言处理中的难题之一——译文/词义消歧,在基于Web搜索的研究思路上进行了初步探索。
Internet is the king of the world at present. To most people nowadays, it is the most important mean to acquire information from Internet by an efficient search engine. There is an insurmountable barrier between understanding different kind of languages in this more and more international information so that the most urgent problem for us is to research machine translation (MT) and cross-language information retrieval (CLIR). There are many hard problems have not been resolved on the research yet, one is how to select right target language translation while facing an ambiguity source language word, which is so-called the problem of word translation disambiguation (WTD). WTD and its similar task - word sense disambiguation (WSD) in mono-lingual category are important and hard in the research of nature language processing (NLP) and are always the basis of it.
Facing the current situation, through the comparision and analysis on all kinds of unsupervised disambiguation methods, this thesis considers that the key problem of unsupervised WTD/WSD is in three fields: knowledge acquisition, data sparseness and the construction of biligual semantic resourse. So, aiming at the study of knowledege acquisition and conquer data sparseness which are the core problems in unsupervised WTD/WSD, this thesis introduces a series of creative methods on unsupervised WTD knowledge acquisition. All of the methods are evaluated on the international golden set of semantic evalution and are all superior to the best comparable systems and get the state of arts result. Another key problem in WTD - bilingual semantic dictionary auto-construction is also studied.
In detail, this thesis is arranged as following:
1. Automatic sense-tagged corpus acquisition, forms the disambiguation classifier directly and introduces the classifier to WTD are all studied. On the basis of that, the thesis introduces the concept of Equivalent PseudoTranslation (EPT) and the WTD method based on it. Finally, it is compared with other unsupervised systems on Senseval-2 English Lexical Sample task.
2. A fully unsupervised WTD method based on the indirect association(IA) between bilingual words and Web mining is introduced through our investigation in biligual parallel corpus. Four methods of computing IA on Web are designed and three different kind of decision strategics are used during the disambiguation process. Futhermore, the thesis considers the Web as a special semantic lexicon so that the relatedness between bilingual words (WBR) could be defined and computed directly by using Page Counts which Web search engine returned. After testing WBR on a revised golden data set and proving its feasible, a fully unsupervised method is designed for WTD. Both the IA method and the WBR method of WTD are tested on Semeval 2007 task 5- Multilingual Chinese-English Lexical Sample task and made comparison on it.
3. An unsupervised WTD method based on the N-gram language model(LM) and Web mining is introduced after the observation of varies series of synonyms in different sentences. On the basis of the supposition that―different sense, different N-gram pattern‖, the model make disambiguation by using LM knowledge not by semantic knowledge. After testing on the same golden set for comparison with other systems, it shows that the performance of the proposed model is excellent and out-performs all other comparable unsupervised systems. Detail comparison and combining upperbound between LM based model and semantic based model is discussed either
4. A methods that can generate a WTD application-oriented bilingual semantic dictionary automatically from WordNet, HowNet and an large-scale blingual parallel corpus is studied. The method mainly uses similarity of WordNet and HowNet between words to filter statistic noise data between the processing of word alignment. Finally, it forms a bilingual semantic dictionary which has imformation of these three resources: WordNet, HowNet and bilingual parallel corpus.
In brief, basiclly, a complete set of knowledge acquisition-oriented WTD solution methods has been established, especially the Web-based methods which explored the hard problem in NLP - WTD/WSD on the Web search way.
引文
1李涓子.汉语词义消歧方法研究.清华大学博士论文. 1999
2 Dekai Wu, Xuanyin Xia. Large-scale Automatic Extraction of an English-Chinese Translation Lexicon. Machine Translation. 1995, 9(3-4): 285-313
3 E. Weaver, Warren (1949). Translation. Mimeographed, 12 pp., July 15, 1949. Reprinted in Locke, William N. and Booth, A. Donald (1955) (Eds.), Machine translation of lan-guages. John Wiley & Sons, New York, 15-23
4 P.Brown, S.Della Pietra, V.Della Pietra, R.Mercer. Word sense disambiguation using statistical methods. Proceedings of the 29th Meeting of the Association for Computational Linguistics (ACL-91), Berkley, C.A, 1991: 264-270
5 Cavalli-Sforza, Czuba, Mitamura, Nyberg. Challenges in Adapting an Interlingua for Bidirectional English-Italian Machine Translation. John S. White (Eds). Proceedings of 4th Conference of the Association for Machine Translation in the Americas (AMTA). 2000: 169-178
6 Cavalli-Sforza, Soudi, Mitamura. Arabic Morphology Generation Using a Concatenative Strategy. 1st Meeting of the North American Chapter of the Association for Computational Linguistic (NAACL). Seattle, USA, 2000:86-93
7 Dilek Zeynep Hakkani, G?khan Tür, Kemal Oflazer, Teruko Mitamura, Eric Nyberg. An English-to-Turkish Interlingual MT System. Proceedings of 3rd Conference of the Association for Machine Translation in the Americas (AMTA). 1998: 83-94
8刘小虎.英汉机器翻译中词义消歧的研究.哈尔滨工业大学博士学位论文. 1997
9张晶.基于目标语的译文选择.哈尔滨工业大学博士学位论文. 2002
10 Kilgarriff, Adam and Martha Palmer, editors. Special Issue on SENSEVAL, Journal Computers and the Humanities. Kluwer Academic Publishers, vol. 34(1-2), 2000
11 Edmonds, Philip and Adam Kilgarriff, editors. Special Issue on Evaluating Word Sense Disambiguation Systems. Natural Language Engineering. Cambridge University Press, vol. 8(4), 2002,
12 Rada Mihalcea and Phil Edmonds, editors. Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, Association for Computational Linguistics Conference (ACL2004). 2004:44~48
13 Eneko Agirre, Llú?s M`arquez and Richard Wicentowski, editors. SemEval 2007: Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics Conference. Prague, Czech Republic. June, 2007:23~24
14 Carpuat, M. and Wu, D.Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation. In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP), Jeju Island, Republic of Korea, 2005:122–127
15 Carpuat, M. and Wu, D. Word sense disambiguation vs. statistical machine translation.43rd ACL Meeting, Ann Arbor. 2005:387~394
16 David Vickrey, Luke Biewald, Marc Teyssier and Daphne Koller. Word-sense disambiguation for machine translation. In Proceedings of the HLT/EMNLP. Vancouver, BC, 2005:771–778
17 Yee Seng Chan, Hwee Tou Ng and David Chiang. Word Sense Disambiguation Improves Statistical Machine Translation. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic, June 2007:33–40
18 Carpuat, M. and Wu, D. Improving Statistical Machine Translation using Word Sense Disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, June, 2007:61–72
19 H. H. Chen, G. W. Bian and W. C. Lin. Resolving Translation Ambiguity and Target Polysemy in Cross-Language Information Retrieval. Proceedings of ACL‘99, 1999: 215~222
20 B. J. Kang, K. S. Choi. Two approaches for the resolution of word mismatch problem caused by English words and foreign words in Koreaninformation retrieval. Proceedings of the fifth international workshop on Information retrieval with Asian languages, 2000: 133~140
21 H. C. Seo, S. B. Kim, H. C. Rim, S. H. Myaeng. Improving Query Translation in English-Korean Cross-Language Information Retrieval. Information Processing and Management, 2005, 41(3): 507~522
22 Nancy Ide and Jean Véronis. Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art. Computational Linguistics. 1998, 24(1): 1~40
23 Wilks, Yorick, Mark Stevenson. Word sense disambiguation using optimised combinations of knowledge sources. In: ACL eds. International Conference of Conputational Linguistics. Mentreal, Canada: ACL, 1998: 1398-1402
24 Small, Steven L. and Garrison W. Cottrell, Parsing and comprehending with word experts. Wendy Lenhert and Martin Ringle, eds., Strategies for Natural Language Processing. Lawrence Erlbaum and Associates, Hillsdale, NJ. 1982: 89-147
25 Eric Brill. Transformation-Based Error-Driving Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging. Computational Linguistics. 1995, 21(4): 543-566
26 Robert A. Amsler, John S. White. Development of a computational methodology for deriving natural language semantic structures via analysis of machine-readable dictionaries, Final report on NSF project MSC77-01315, University of Texas at Austin, Austin, TX, 1979
27 Michael Lesk, Automated Sense Disambiguation Using Machine-readable Dictionaries: How to tell a pine cone from an icee cream cone. In Procedings of the 1986 SIGDOC Conference, Toronto, Canada, June, 1986: 24-26
28 J. Cowie, J. Guthrie, and L. Guthrie. Lexical disambiguation using simulated annealing. In Proceedings of the International Conference on Computational Linguistics. COLING. 1992:359-365
29 D. Yarowsky. Word sense disambiguation using statistical models of Roget‘s categories trained on large corpora. In Proceedings of the International Conference on Computational Linguistics, COLING.1992:454-460
30 G. Grefenstette. Evaluation Techniques for Automatic Semantic Extraction: Comparing Syntatic and Window–based Approaches. In Proceedings of the ACL SIGLEX Workshop on Acquisition of Lexical Knowledge from Text, 1993:143~153
31 Alpha, K, Luk. Statistical sense disambiguation with relatively small corpus using dictionary definitions. In: Proc of 33rd Annual Meeting of the Association for Computational Linguistics. Massachusetts Institute of Technology, Cambridge, Massachusetts, 1995:181~188
32 Agirre, Eneko, German Rigau, A proposal for word sense disambiguation using conceptual distance, Proceedings of the 1st International Conference on Recent Advances in Natural Language Processing, Velingrad, 1995
33 Philip Resnik. Disambiguating noun groupings with respect to WordNet senses. Proceedings of the 3rd Workshop on Very Large Corpora, MIT, 1995:54~68
34 S. Banerjee, T. Pedersen, An adapted Lesk algorithm for word sense disambiguation using WordNet, in: Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City, 2002:136~145
35 R. Mihalcea, P. Tarau, and E. Figa. PageRank on semantic networks, with application to word sense disambiguation. In Proceedings of the 20st International Conference on Computational Linguistics (COLING 2004), Geneva, Switzerland. 2004:1126~1132
36 S. Brin and L. Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7):107–117
37 Ravi Sinha and Rada Mihalcea, Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity, in Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2007), Irvine, CA, September 2007:363~369
38 Abney, Steven P. Statistical methods. In Encyclopedia of Cognitive Science. Nature Publishing Group, Macmillian. 2005:283~293
39 Lucia Specia, Maria das Gracas Volpe Nunes, Gabriela Castelo Branco Ribeiro, and Mark Stevenson. Multilingual versus monolingual WSD. InEACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together. Trento, Italy, April 2006:33~40
40 Specia, L.; Nunes, M.G.V.; Stevenson, M. (2006c). Translation Context Sensitive WSD. 11th Annual Conference of the European Association for Machine Translation, EAMT-2006. Oslo, June 19-20, 2006:227~232
41 Gerard Escudero Bakx. Machine Learning Techniques for Word Sense Disambiguation. Universitat Politμecnica de Catalunya. PhD thesis. Barcelona, May 22, 2006
42 Rada Mihalcea. Word Sense Disambiguation. Encyclopedia of Machine Learning. Springer. 2007
43卢志茂.无指导词义消歧方法研究.哈尔滨工业大学博士学位论文. 2006
44 W. Gale, K. W. Church, and D. Yarowsky. One Sense Per Discourse. In Proceedings of the DARPA Speech and Natural Language Workshop, 1992:233~237
45 C. Leacock, G. Towell, and E. Voorhes. Towards Building Contextual Representations of Word Sense Using Statistical Models. In Proceedings of SIGLEX Workshop on Acquisition of Lexical Knowledge from Text, Association for Computational Linguistics. 1993:10~20
46 T. Pedersen and R. Bruce. A New Supervised Learning Algorithm for Word Sense Disambiguation. In Proceedings of the National Conference on Artificial Intelligence, AAAI, 1997:604~609
47 G. Escudero, L. M`arquez, and G. Rigau. Naive Bayes and Exemplar–Based Approaches to Word Sense Disambiguation Revisited. In Proceedings of the European Conference on Artificial Intelligence, ECAI, 2000:421~425
48 D. Yuret. Some Experiments with a Naive Bayes WSD System. In Proceedings of the International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval–3, 2004:265~268
49 G. Chao and M. G. Dyer. Maximum Entropy Models for Word Sense Disambiguation. In Proceedings of COLING-2002, Taipei, 2002:431~437
50 A. Su′arez and M. Palomar. A Maximum Entropy–based Word Sense Disambiguation System. In Proceedings of the International Conference on computational Linguistics, COLING, 2002:297-313
51 Tanja Gaustad. A Lemma-Based Approach to a Maximum Entropy Word Sense Disambiguation System for Dutch. In Proceedings of COLING-2004, Taipei, 2004:778~784
52 D. Yarowsky. Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL, 1994:88~95
53 R. L. Rivest. Learning Decision Lists. Machine Learning, 2, 1987:229~246
54 D. Yarowsky. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, 1995:189~196
55 David Yarowsky. Hierarchical Decision Lists for Word Sense Disambiguation. Computer and the Humanities, 2000, 34:179~186
56 Black, Ezra. An experiment in computational discrimination of englishword senses. IBM Journal of Research and Development, 1988, Vol. 32(2): 185~194
57 R. J. Mooney. Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, 1996:82~91
58 Ted Pedersen. A Decision Tree of Bigrams is an Accurate Predictor of Word Sense. In Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, 2001:79~86
59 Ted Pedersen. Evaluating the Effectiveness of Ensembles of Decision Trees in Disambiguating Senseval Lexical Samples. In the Proceedings of the Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia, July 11, 2002:81~87
60 H. Sch¨utze. Dimensions of Meaning. In Proceedings of Supercomputing‘92. 1992:787~796
61 Schutze, H. Word space. In Hanson, S. J., Cowan, J. D., & Giles, C. L. Eds., Advances in Neural Information Processing Systems. Morgan Kaufmann Publishers, San Mateo CA.1993:895~902
62 D. Yarowsky, S. Cucerzan, R. Florian, C. Schafer, and R. Wicentowski.The Johns Hopkings Senseval–2 System Descriptions. In Proceedings of the International Workshop on evaluating Word Sense Disambiguation Systems, Senseval–2, 2001:163~166
63 H. T. Ng. Exemplar-Base Word Sense Disambiguation: Some Recent Improvements. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, 1997
64 V. Hoste, A. Kool, and W. Daelemans. Classifier Optimization and Combination in the English All Words Task. In Proceedings of the International Wordshop on Evaluating Word Sense Disambiguation Systems, 2001:84~86
65 B. Decadt, V. Hoste, W. Daelemans, and A. van den Bosch. GAMBL, Genetic Algorithm Optimization of Memory–Based WSD. In Proceedings of the International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval–3, 2004:108~112
66 R. Mihalcea and E. Faruque. SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, 2004:155~158
67 G. Escudero, L. M`arquez, and G. Rigau. A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation. In Proceedings of the Computational Natural Language Learning Workshop, CoNLL, 2000:31~36
68 C. Cabezas, P. Resnik, and J. Stevens. Supervised Sense Tagging using Support Vector Machines. In Proceedings of the International Workshop on Evaluating Word Sense Disambiguation Systems, 2001:59~62
69 M. Murata, M. Utiyama, K. Uchimoto, Q. Ma, and H. Isahara. Japanese Word Sense Disambiguation Using the Simple Bayes and Support Vector Machine Methods. In Proceedings of the International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval–2, 2001:135~138
70 Y. K. Lee and H. T. Ng. An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing,EMNLP, 2002:41~48
71 E. Agirre and D. Mart′inez. The Basque Country University system: English and Basque tasks. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, 2004:44~48
72 G. Escudero, L. M`arquez, and G. Rigau. TALP System for the English Lexical Sample Task. In Proceedings of the 3rd International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval-3, Association for Computational Linguistics. 2004:113~116
73 Y.K.Lee, H.T.Ng, andT.K.Chia. Supervised Word Sense Disambiguation with Support Vector Machines and Multiple Knowledge Sources. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:137~140
74 C. Strapparava, A. Gliozzo, and C. Giuliano. Pattern Abstraction and Term Similarity for Word Sense Disambiguation: IRST at Senseval–3. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:229~234
75 M. Carpuat, W. Su, and D. Wu. Augmenting Ensemble Classification for Word Sense Disambiguation with a Kernel PCA Model. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:88~92
76 C. Grozea. Finding optimal parameter settings for high performance word sense disambiguation. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:125~128
77 M. Popescu. Regularized Least–Squares Classification for Word Sense Disambiguation. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:209~212
78 A. Gliozzo, C. Giuliano and C. Strapparava. Domain Kernels forWordSense Disambiguation. In Proceedings of the 43th Annual Meeting of the Association for Computational Linguistics (ACL), July, 2005:403~410
79 A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka. Selective Sampling for Example-Based Word Sense Disambiguation. Computational Linguistics, 1998, 24(4):573~597
80 T. Pedersen and R. Bruce. Knowledge Lean Word–Sense Disambiguation. In Proceedings of the Conference of the American Association for Artificial Intelligence, AAAI, 1998:800~805
81 T. Pedersen. A Simple Approach to Building Ensembles of Na?ve Bayesian Classifiers for Word Sense Disambiguation. In Proceedings of the First Annual Meeting of the North American Chapter of the Association for Computational Linguistics, Seattle, WA, May, 2000: 63~69
82 Ted Pedersen. A Decision Tree of Bigrams is an Accurate Predictor of Word Sense. In Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, 2001: 79~86
83 Florian Radu, Silviu Cucerzan, Charles Schafer, and David Yarowsky. Combining classifiers for word sense disambiguation. Natural Language Engineering. 2002, 8(4):327~341
84 Florian Radu and David Yarowsky. Modeling consensus: Classifier combination for word sense disambiguation. In Proceedings of EMNLP-2002. 2002:25~32
85 Dan Klein, KristinaToutanova, H.Tolga Ilhan, Sepandar D. Kamvar and Christopher D. Manning. Combining Heterogeneous Classifiers forWord-Sense Disambiguation. Proceedings of the SIGLEX/SENSEVAL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia, July 2002:74~80
86 G. Escudero, L. M`arquez, and G. Rigau. Boosting Applied to Word Sense Disambiguation. In Proceedings of the European Conference on Machine Learning, ECML, 2000:129~141
87 Brown, Gavin, Jeremy Wyatt, Rachel Harris, and Xin Yao. Diversity creation methods: A survey and categorization. Information Fusion Journal Special issue on Diversity in Multiple Classifier Systems. 2004:5~20
88 G.A. Miller and W.G. Charles. Contextual correlates of semantic similarity.Language and Cognitive Processes, 1991, 6(1):1~28
89 T. Pedersen and R. Bruce. Distinguishing word senses in untagged text. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, Providence, RI, August, 1997:197~207
90 T. Pedersen and R. Bruce. Knowledge lean word sense disambiguation. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, WI, July, 1998: 800~805
91 Henrich. Schütze. Automatic Word Sense Discrimination. Computational Linguistics. 1998, 24(1): 97~123
92 A. Purandare and T. Pedersen. Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces. In Proceedings of the Conference on Computational Natural Language Learning, Boston, MA, 2004:41~48
93 A. Purandare and T. Pedersen. Improving Word Sense Discrimination with Gloss Augmented Feature Vectors. In the Proceedings of the Workshop on Lexical Resources for the Web and Word Sense Disambiguation, Puebla Mexico, November 22, 2004
94 Cheng Niu, Wei Li, et al. Context Clustering for Word Sense Disambiguation Based on Modeling Pairwise Context Similarities. In Proceedings of the 42th ACL, The Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, Association for Computational Linguistics. July 2004:187~190
95 C. Leacock, M. Chodorow, G.A. Miller, Using Corpus Statistics and WordNet Relations for Sense Identification. Computational Linguistics, 1998, 24(1):147~166
96 R. Mihalcea and I. Moldovan. A Method for Word Sense Disambiguation of Unrestricted Text. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics, ACL, 1999:152~158
97 R. Mihalcea and I. Moldovan. An Automatic Method for Generating Sense Tagged Corpora. In Proceedings of the Conference of the American Association for Articial Intelligence, AAAI, 1999:461~466
98 R. Mihalcea. Bootstrapping Large Sense Tagged Corpora. In Proceedings of the International Conference on Languages Resources and Evaluation, LREC, 2002:1407~1411
99 E. Agirre and D. Mart‘inez. Unsupervised WSD based on automatically retrieved examples: The importance of bias. In Proceedings of the International Conference on Empirical Methods in Natural Language Processing, EMNLP, 2004:25~32
100 Xinglong Wang and John Carroll. Word Sense Disambiguation Using Sense Examples Automatically Acquired from a Second Language. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, October 2005:547~554
101 X. Wang and D. Martinez. Word sense disambiguation using automatically translated sense examples. In Proceedings of EACL 2006 Workshop on Cross Language Knowledge Induction, Trento, Italy. 2006:45~52
102 D Martinez, E Agirre, X Wang. Word relatives in context for word sense disambiguation. Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), 2006:42~50
103 I. Dagan, A. Itai and U. Schwall. Two Languages are More Informative than one. Proceedings of the 29th Annual Meeting of the ACL,18-21 Berkeley, California, 1991:130~137
104 I. Dagan and Alon Itai. Word Sense Disambiguation Using a Second Language Monolingual Corpus. Computational Linguistics, 1994, 20(4):56~596
105 G. Kikui. Resolving Translation Ambiguity Using Non-parallel Bilingual Corpora. In Proceedings of ACL‘99 Workshop on Unsupervised Learning in Natural Language Processing, 1999:31~36
106 H. Kaji and Y. Morimoto. Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora. In Proceedings of COLING-2002, Taipei, 2002:155~161
107 Ming Zhou, Yuan Ding, Changning Huang. Improving Translation Selection with a New Translation Model Trained by Independent Monolingual Corpora. Computational Linguistics and Chinese Language Processing, February 2001, Vol. 6(1):1~26
108 HYUN AH LEE, JUNTAE YOON AND GIL CHANG KIM. Translation Selection by Combining Multiple Measures for Sense Disambiguation andWord Selection International Journal of Computer Processing of Oriental Languages, 2003, Vol. 16(3):1~21
109 Philipp Koehn and Kevin Knight. Estimating word translation probabilities from unrelated monolingual corpora using the EM algorithm. In Proceedings of the National Conference on Articial Intelligence, pages, Austin, TX, USA. 2000:711~715
110 N. Ide. Parallel Translations and Sense Discriminators. In Proceedings of the ACLSIGLEX Workshop on Standardizing Lexical Resources, 1999: 52~61
111 Nancy Ide. Cross-lingual Sense Determination: Can it work? In Computers and the Humanities: Special Issue on Senseval, 2000, 34:147~152
112 Nancy Ide, T. Erjavec, et al. Automatic Sense Tagging Using Parallel Corpora. Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium, Tokyo, 2001:83~89
113 Nancy Ide, Tomaz Erjavec, et al. Sense Discrimination with Parallel Corpora. In Proceedings of the ACL SIGLEX Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, 2002:54~60
114 Hwee Tou Ng, Bin Wang, et al. Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study. In Proceedings of the 41th Annual Meeting of the Association for Computational Linguistics, 2003:455~462
115 Mona Diab and Philip Resnik. An Unsupervised Method for Word Sense Tagging Using Parallel Corpora. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002:255~262
116 Mona Diab. Word Sense Disambiguation within a Multilingual Framework. University of Maryland College Park. PhD Thesis, USA, 2003
117 Mona Diab. An Unsupervised Approach for Boot-strapping Arabic Word Sense Tagging. Proceedings of Computational Approaches to Arabic Based Script Languages, COLING 2004. Geneva, Switzerland, 2004:43~50
118 Mona Diab. Relieving the Data Acquisition Bottleneck in Word Sense Disambiguation. In Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics, 2004:303~310
119 Yoshua Bengio and Christopher Kerorvant. Extracting Hidden Sense Probabilities from Bitexts. Technical report, TR 1231, Departementd‘informatique et recherche operationnelle, Universite de Montreal. 2003
120 I. Bhattacharya, L. Getoor, Y. Bengio. Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models. In Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics, 2004:287~294
121 P. Resnik and D. Yarowsky. A Perspective on Word Sense Disambiguation Methods and Their Evaluation. In Proceedings of the ACL SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What, and How? 1997:79~86
122 Franz Josef Och and Hermann Ney. Improved Statistical Alignment Models. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, 2000:440~447
123 Mirella Lapata and Frank Keller. Web-based Models for Natural Language Processing. ACM Transactions on Speech and Language Proccessing, February 2005, Vol. 2(1):1~30
124 A. Kilgarriff. ?Web as Corpus‘, in Proc. Corpus Linguistic Conf. UCREL-Lancaster Univ, UK. 2001:342~344
125 Resnik, P. and Smith, N. A. The web as a parallel corpus. Computational Linguistics. 2003,Vol. 29(3):349~380
126 Chklovski, T., and Mihalcea, R. Building a sense tagged corpus with Open Mind Word Expert. In Proceedings of the ACL 2002 Workshop on‖Word Sense Disambiguation: Recent Successes and Future Directions‖Philadelphia, July 2002:116~122
127 Rada Mihalcea, Using Wikipedia for Automatic Word Sense Disambiguation, in Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL 2007), Rochester, April 2007:196~203
128 Chao, G., and Dyer, M. Word sense disambiguation of adjectives using probabilistic networks. In Proceedings of the 18th International Conference on Computational Linguistics (COLING 2000), Saarbrucken, 2000:152~158
129 Turney, P. Mining the Web for synonyms:PMI-IR versus LSA on TOEFL. Proceedings of the Twelfth European Conference on Machine Learning. Berlin: Springer-Verlag. 2001:491~502
130 Turney, P. Word Sense disambiguation by web mining for word co-occurrence probabilities. In Proc. SENSEVAL-3. Barcelona, Spain, July 2004:239-242
131 Rosso, P., Montes, M., Buscaldi, D., Pancardo, A., and Villase, A. Two Web-based Approaches for Noun Sense Disambiguation. Int. Conf. on Comput. Linguistics and Intelligent Text Processing, CI-CLing-2005, Springer Verlag, LNCS (3406), Mexico D.F., Mexico, 2005:261-273
132 YANG Che-Yu. Word sense disambiguation using semantic relatedness measurement. Journal of Zhejiang University SCIENCE A. 2006, Vol.7 (10):1609~1625
133 Blum, A., and Mitchell, T. Combining labeled and unlabeled data with co-training. In Proceedings of the lth Annual Conference on Computational Learning Theory (COLT-98). 1998:92~100
134 R. Mihalcea and D. I. Moldovan. An Iterative Approach to Word Sense Disambiguation. In Proceedings of FLAIRS-2000, Orlando, FL, May, 2000: 219~223
135 R. Mihalcea. Co-training and Self-training for Word Sense Disambiguation. Proceedings of the European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, August 2004
136 Thanh Phong Pham, Hwee Tou Ng, and Wee Sun Lee. Word Sense Disambiguation with Semi-Supervised Learning. In Proc. of the 20th National Conference on Artificial Intelligence (AAAI 2005), 2005:1093~1098
137 Seong-Bae Pare, Byoung-Tak Zhang, et al. Word Sense Disambiguation by Learning Decision Trees from Unlabeled Data. Kluwer Academic Publishers. Manufactured in The Netherlands. Applied Intelligence, 2003, Vol.19:27~38
138 D. Yarowsky, "One Sense Per Collocation," in Proceedings, ARPA Human Language Technology Workshop, Princeton, 1993:266~271
139 K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proceedings of the Conference on Information and Knowledge Management CIKM-2000, McLean, Virginia. 2000:86~93
140 Cong Li and Hang Li. Word translation disambiguation using bilingualbootstrapping. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002:343-351
141 Zheng-Yu Niu, Dong-Hong Ji and Chew Lim Tan. Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning. In Proceedings of the 43th Annual Meeting of the Association for Computational Linguistics (ACL), July 2005:395~402
142 Zheng-Yu Niu, Dong-Hong Ji, Chew Lim Tan. A semi-supervised feature clustering algorithm with application to word sense disambiguation. Human Language Technology Conference Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Vancouver, British Columbia, Canada, 2005:907~914
143鲁松,白硕,黄雄,张健.基于向量空间模型的有导词义消歧.计算机研究与发展. 2001, Vol. 38(6):662-667
144卢志茂,刘挺,张刚,李生.基于依存分析改进贝叶斯模型的词义消歧.高技术通讯. 2003, Vol. 13(5):1~7
145卢志茂,刘挺,郎君,李生.神经网络和贝叶斯模型在汉语词义消歧上的对比研究.高技术通讯. 2004, Vol. 14(8):15~19
146荀恩东,赵铁军,李生. BT863Ⅱ英汉机器翻译中译文选择方法.情报学报. 1999, Vol. 18(2):99~104
147荀恩东,李生等.基于汉语二元同现的统计词义消歧方法研究.高技术通讯. 1998, Vol. 8(10):21~25
148张晶,李生,姚建民,赵铁军.基于目标语词汇组合合理性评价的译文选择模型.计算机研究与发展. 2003,Vol. 40(9):1361-1367
149黄昌宁,李涓子.词义排歧的一种语言模型.语言文字应用. 2000, 3:85~90
150陈清才,王晓龙.一种基于词矢量的汉语语义量化模型.计算机研究与发展. 2001, Vol. 38(2): 208~212
151鲁松,白硕,黄雄,基于向量空间模型中义项词语的无导词义消歧.软件学报. 2002,Vol. 13(6):1082~1089
152陈浩,何婷婷,姬东鸿.基于k-means聚类的无导词义消歧.中文信息学报. 2005,Vol. 14(9):10-16
153杨尔弘,张国清,张永奎.基于义原同现频率的汉语词义排歧方法.计算机研究与发展. 2001, Vol. 38(7):834~837
154 Zhimao Lu, Haifeng Wang, Jianmin Yao, Ting Liu, Sheng Li, An EquivalentPseudoword Solution to Chinese Word Sense Disambiguation, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL(ACL2006). Sydney, Australia, 2006:457~464
155李涓子,黄昌宁,杨尔弘.一种自组织的汉语词义排歧方法.《中文信息学报》. 1999,Vol. 13(9):1~8
156全昌勤,何婷婷,姬东鸿,刘辉.从搭配知识获取最优种子的词义消歧方法.中文信息学报. 2005,Vol. 19(1):30~35
157 Philip Resnik. Mining the Web for Bilingual Text. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, 1999:527~534
158 Rada Mihalcea, Word Sense Disambiguation Using Pattern Learning and Automatic Feature Selection, in Journal of Natural Language and Engineering (JNLE), December 2002, Vol. 8:343~358
159 A. Montoyo, A. Suarez, G. Rigau, and M. Palomar. Combining knowledge- and corpus-based word sense disambiguation methods. Journal of Artificial Intelligence Research, 2005, Vol. 23:299–330
160 Good, Irwin J. The population frequencies of species and the distribution of population parameters. Biometrika, 1953, Vol. 40(3/4):237-264
161 Brown, Peter F.,Della Pietra, Vincent J.,deSouza, Peter V.,Lai,Jennifer C.and Mercer Robert L.―Class-based ngram models of natural language.‖Computational Linguistics, 1992, Vol. 18(4):467-479
162 Dagan, Ido; Marcus, Shaul; and Markovitch, Shaul. Contextual word similarity and estimation from sparse data. Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, Columbus, Ohio, June 1993: 22-26
163 Peng Jin, Yunfang Wu and Shiwen Yu, 2007. SemEval-2007 Task 5: Multilingual Chinese-English Lexical Sample. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:19~23,
164 Saif Mohammad, Graema Hirst, Philip Resnik. TOR, TORMD:Distribtional Profiles of Concepts for Unsupervised Word Sense Disambiguation. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:326~333
166 William A. Gale, K. W. Church, and D. Yarowsky. Word on Statictical Methods for Word Sense Disambiguation. In Robert Goldman, Peter Norvig. Eugene Charniak, and Bill Gale (eds.). Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, Menlo Park, CA. AAAI Press, 1992:54~60
167 H. Schütze. Context Space. In Robert Goldman, Peter Norvig. Eugene Charniak, and Bill Gale (eds.). Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, Menlo Park, CA. AAAI Press, 1992:113~112
168 Gaustad, Tanja. Statistical corpus-based word sense disambiguation: Pseudowords vs. real ambiguous words. In Companion Volume to the Proceedings of the 39th Annnual Meeting of the Associations for Computational Linguistics (ACL/EACL 2001)– Proceedings of the Student Workshop. Toulouse, France. 2001:61~66
169 G.K.Zipf. Psycho-biology of Language. The MIT Press, 1935
170 David Yarowsky and Radu Florian. Evaluating sense disambiguation across diverse parameter spaces. Nautral Language Engineering. 2002, Vol. 8(4):293~310
171 Agirre, E., Martinez, D. Exploring Automatic Word Sense Disambiguation with Decision Lists and the Web. Proceedings of the COLING Workshop on Semantic Annotation and Intelligent Content. Saarbrticken, Germany. 2000:17~25
172 Gonzalo, J., Verdejo, F., Chugar, I. The Web as a Resource for WSD. In: 1st MEANING Workshop, Spain. 2003
173 Yee Seng Chan and Hwee Tou Ng. Estimating Class Priors in Domain Adaptation for Word Sense Disambiguation. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL. Sydney, July, 2006:89~96,
174 Vinci Liu and James R.Curran. Web Text Corpus for Natural LanguageProcessing. Proceedings of EACL-06, 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, IT, 2006:233~240
175 Frank Keller and Mirella Lapata. Using the web to obtain frequencies for unseen bigrams. Computational Linguistics. 2003, Vol. 29(3):459~484
176 Danushka Bollegala, Yutaka Matsuo Mitsuru lshizuka. Measuring Semantic Similarity between Words Using Web Search Engines. In Proceedings of the Sixteenth International World Wide Web Conference (WWW2007). Banff, Alberta, CANADA. May 8-12, 2007:757~767.
177 Church, K.W., Hanks, P. Word Association Norms, Mutual Information and Lexicography. In: Proceedings of the 27th Annual Conference of the Association of Computational Linguistics. 1989:76~83
178 F. Smadja, K. R. McKeown, and Vasileios Hatzivassiloglou. Translating Collocations for Bilingual Lexicons: A statistical approach. Computational Linguistics. 1996, Vol. 22(1):1~38
179 W. A. Gale, K. W. Church. Identifying Word Correspondences in Parallel Texts.In: Proceedings of the 4th DARPA workshop on Speech and Natual Language. 1991:152~157
180 T. Dunning. Accurate Methods for the Statistics of Surprise and Coincidence. Computational Linguistics. 1993, Vol. 19(1):61~74
181 I. D. Melamed. Automatic Construction of Clean Broad-Coverage Translation Lexicons. In: proceedings of 2nd Conference of the Association for Machine Translation in the Americas. Montreal, Canada. 1996:125~134
182 I. D. Melamed. Empirical Methods for MT Lexicon Development. Proceedings of 3rd Conf. Of Association for Machine Translation in the Americas. 1998:18~30
183 Shudong Huang and David Graff. Chinese-english translation lexicon version 3.0. Linguistic Data Consortium. 2002
184 PengYuan Liu, TieJun Zhao and MuYun Yang. HIT-WSD: Using Search Engine for Multilingual Chinese-English Lexical Sample Task. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:169~172
185 Shinzato, K and Torisawa, K. Acquiring hyponymy relations from web documents. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. Boston, 2004:73-80
186 Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka. Measuring Semantic Similarity between Words Using Web Search Engines. In Proceedings of the Sixteenth International World Wide Web Conference (WWW2007). Banff, Alberta, CANADA. May 8-12, 2007:757~767
187 Ted Pedersen, Satanjeev Banerjee, Siddharth Patwardhan. Maximizing Semantic Relatedness to Perform Word Sense Disambiguation. 2005. Available at http://www.msi.umn.edu/general/Reports/rptfiles/2005-25.pdf. University of Minnesota Supercomputing Institute Research Report UMSI 2005/25, March.
188 G. Miller and W. Charles. Contextual correlates of semantic similarity. Language and Cognitive Processes. 1998, Vol. 6(1):1~28
189 H. Rubenstein and J. Goodenough. Contextual correlates of synonymy. Communications of the ACM. 1965, Vol. 8:627~633
190 M. Sahami and T. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proc. of 15th International World Wide Web Conference.2006:377~386
191 Deniz Yuret. KU: Word Sense Disambiguation by Substitution. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:207~214,
192 D. Hiemstfa. A Linguistically Motivated Probabilistic Model of Information Retrieval. Proceedings of the 2th European Conference on Research and Advance Technology for Digital Libraries, 1998:569~584
193 Shixin Yu: Feature Selection and Classifier Ensembles: a Study on Hyperspectral Remote Sensing Data. http://142.129.203.3/Visielab/Thesis/Shixin/Thesis_Yu.Pdf. 2003
194 Ted Pedersen, a baseline methodology for word sense disambiguation, in the Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City. February,2002:17~23
195 J.Kittler, M.Hatef, R.P. Duin et al. On Combining Classifiers. IEEE Trans. Patter Anal. Mach. Intell. 1998:226~239
196 L.breiman. Bagging Predictors. Machine Learning, 1996, Vol. 24(2):123~140
197 L. Xu, A. Krzyzak, C.Y.Suen. Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Transactions on Systems.1992:418~435
198陈群秀,现代汉语述语动词机器词典的扩充和槽关系研究,《语言文字应用》,2001 , Vol. (4):98~104
199陈力为,袁琦.《中文信息处理应用平台工程》,电子工业出版社1995年版
200吴蔚天.《汉语计算语义学——关系,关系语义场和形式分析》,电子工业出版社. 1999
201 Qun Liu and Shiwen Yu. TransEasy: A Chinese-English Machine Translation System Based on Hybrid Approach. In David Farwell et al., editors, Machine Translation and the Information Soup. Third Conference of the Association for Machine Translation in Americas (AMTA). Langhorne, PA. USA, October 1998:514~517
202 Copestake A. An approach to building the hierarchical element of a lexical knowledge base from a machine readable dictionary. Proceedings of the First International Workshop on Inheritance in Natural Language Processing. Tilburg. 1990:19~29
203 Riloff E, Shepherd J. A corpus-based approach for building semantic lexicons. Proceedings of the 2nd Conference on Empirical Methods in Matural Language Processing. Rhode Island, USA. 1997:117~124
204 Richardson S., Dolan W., and Vandervende L. MindNet: acquiring and structuring semantic information from text. Proc. of COLING98, Montreal , CA. 1998.1098~1102
205 Craig J. A. McEwan, Iadh Ounis, and Ian Ruthven. Building bilingual dictionaries from parallel web documents. In Proc. of ECIR, 2002:303~323
206 Dorr, B. J., Levow, G.-A., and Lin, D. Building a Chinese-English Mapping between Verb Concepts for Mul-tilingual Applications. In Proceedings of the Fourth Conference of the Association for Machine Translation in theAmericas (AMTA). Cuernavaca, Mexico, 2000:1~12
207 Dorr, B. J. Gina-Anne Levow, Dekang Lin, and Scott Thomas. Chinese-English Semantic Resource Construction, 2nd International Conference on Language Resources and Evaluation (LREC2000), Athens, Greece, 2000:757~760
208张晶,姚建民,赵铁军,李生.基于WordNet和HowNet建设双语语义词典.高技术通讯. 2001, Vol. 11(12):28~32
209 HSIN-HSI CHEN, CHI-CHING LIN, AND WEN-CHENG LIN, Building a Chinese-English WordNet for Translingual Applications, ACM Transactions on Asian Language Information Processing. June 2002, Vol. 1(2):103~122
210 CHEN, H .H. LIN, C. C., AND LIN, W. C. Construction of a Chinese-English WordNet and its application to CLIR. In Proceedings of the Fifth International Workshop on Information Retrieval with Asian Languages (Hong Kong, Sept.-Oct. 2000). ACM Press, New York, NY, 2000:189~196
211 Huang, Chu-Ren. Elanna I. J. Tseng, Dylan B. S. Tsai and Brian Murphy. Cross-lingual Portability of Semantic relations: Bootstrapping Chinese WordNet with English WordNet Relations. Languages and Linguistics. 2003, Vol. 4(3):509~532
212 Jason S. Chang, Tracy Lin, Geeng-Neng You,Thomas C. Chuang, Ching-Ting Hsieh. Building A Chinese WordNet Via Class-Based Translation Model, Computational Linguistics and Chinese Language Processing, August 2003, Vol. 8(2):61~76
213刘群,李素建.基于《知网》的词汇语义相似度计算.第三届汉语词汇语义学研讨会,台北, 2002
214 Miller, G. A, Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. Introduction to WordNet: An on-line Lexical Database.In Five Papers on WordNet, CSL report, Cognitive Science Laboratory, Princeton University. International Journal of Lexicography. 1990, Vol. 3(4):235~244
215姚天顺,张俐,高竹. WordNet综述.语言文字应用. 2001, 1:27~32
216 Resnik P. Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint 1995 Conference on ArtificialIntelligence. Montreal. 1995:448~453,
217 Lin D. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning. Madison, Wisconsin. 1998:296~304
218 Jiang J., Conrath D. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics. Taiwan. 1997:19~33
219 Leacock C., Chodorow M. Combining local context and WordNet similarity for word sense identification. In Christiane Fellbaum, editor, WordNet: A Lexical Reference System and its Application. MIT Press, Cambridge, MA. 1998:265~283
220 Wu Z., Palmer M, Verb Semantics and Lexical Selection, In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico, 1994:133~138
221 Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi, WordNet Similarity-Measuring the Relatedness of Concepts, In Proceedings of AAAI04, Intelligent Systems Demonstration 2004:1024~1025
222杨沐昀,刘晓月,李生.基于汉英双语语料库的汉英词典编撰研究.情报学报, 2003, Vol. 22(3):310~314
223 Carpuat, M. and Ngai, G. et al. Creating a Bilingual Ontology: A Corpus-Based Approach for Aligning WordNet and HowNet. in Proceedings of the 1st Global WordNet Conference(GWC2002), Mysore, India, 2002
224吕雅娟.基于双语语料库对齐的翻译知识自动获取技术研究.哈尔滨工业大学工学博士学位论文, 2003
225 Alexander Budanitsky, Graeme Hirst. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on Wordnet and Other Lexical Resources, Second meeting of the NAACL, Pittsburgh, USA. 2001:29~34
2 Dekai Wu, Xuanyin Xia. Large-scale Automatic Extraction of an English-Chinese Translation Lexicon. Machine Translation. 1995, 9(3-4): 285-313
3 E. Weaver, Warren (1949). Translation. Mimeographed, 12 pp., July 15, 1949. Reprinted in Locke, William N. and Booth, A. Donald (1955) (Eds.), Machine translation of lan-guages. John Wiley & Sons, New York, 15-23
4 P.Brown, S.Della Pietra, V.Della Pietra, R.Mercer. Word sense disambiguation using statistical methods. Proceedings of the 29th Meeting of the Association for Computational Linguistics (ACL-91), Berkley, C.A, 1991: 264-270
5 Cavalli-Sforza, Czuba, Mitamura, Nyberg. Challenges in Adapting an Interlingua for Bidirectional English-Italian Machine Translation. John S. White (Eds). Proceedings of 4th Conference of the Association for Machine Translation in the Americas (AMTA). 2000: 169-178
6 Cavalli-Sforza, Soudi, Mitamura. Arabic Morphology Generation Using a Concatenative Strategy. 1st Meeting of the North American Chapter of the Association for Computational Linguistic (NAACL). Seattle, USA, 2000:86-93
7 Dilek Zeynep Hakkani, G?khan Tür, Kemal Oflazer, Teruko Mitamura, Eric Nyberg. An English-to-Turkish Interlingual MT System. Proceedings of 3rd Conference of the Association for Machine Translation in the Americas (AMTA). 1998: 83-94
8刘小虎.英汉机器翻译中词义消歧的研究.哈尔滨工业大学博士学位论文. 1997
9张晶.基于目标语的译文选择.哈尔滨工业大学博士学位论文. 2002
10 Kilgarriff, Adam and Martha Palmer, editors. Special Issue on SENSEVAL, Journal Computers and the Humanities. Kluwer Academic Publishers, vol. 34(1-2), 2000
11 Edmonds, Philip and Adam Kilgarriff, editors. Special Issue on Evaluating Word Sense Disambiguation Systems. Natural Language Engineering. Cambridge University Press, vol. 8(4), 2002,
12 Rada Mihalcea and Phil Edmonds, editors. Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, Association for Computational Linguistics Conference (ACL2004). 2004:44~48
13 Eneko Agirre, Llú?s M`arquez and Richard Wicentowski, editors. SemEval 2007: Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics Conference. Prague, Czech Republic. June, 2007:23~24
14 Carpuat, M. and Wu, D.Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation. In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP), Jeju Island, Republic of Korea, 2005:122–127
15 Carpuat, M. and Wu, D. Word sense disambiguation vs. statistical machine translation.43rd ACL Meeting, Ann Arbor. 2005:387~394
16 David Vickrey, Luke Biewald, Marc Teyssier and Daphne Koller. Word-sense disambiguation for machine translation. In Proceedings of the HLT/EMNLP. Vancouver, BC, 2005:771–778
17 Yee Seng Chan, Hwee Tou Ng and David Chiang. Word Sense Disambiguation Improves Statistical Machine Translation. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic, June 2007:33–40
18 Carpuat, M. and Wu, D. Improving Statistical Machine Translation using Word Sense Disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, June, 2007:61–72
19 H. H. Chen, G. W. Bian and W. C. Lin. Resolving Translation Ambiguity and Target Polysemy in Cross-Language Information Retrieval. Proceedings of ACL‘99, 1999: 215~222
20 B. J. Kang, K. S. Choi. Two approaches for the resolution of word mismatch problem caused by English words and foreign words in Koreaninformation retrieval. Proceedings of the fifth international workshop on Information retrieval with Asian languages, 2000: 133~140
21 H. C. Seo, S. B. Kim, H. C. Rim, S. H. Myaeng. Improving Query Translation in English-Korean Cross-Language Information Retrieval. Information Processing and Management, 2005, 41(3): 507~522
22 Nancy Ide and Jean Véronis. Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art. Computational Linguistics. 1998, 24(1): 1~40
23 Wilks, Yorick, Mark Stevenson. Word sense disambiguation using optimised combinations of knowledge sources. In: ACL eds. International Conference of Conputational Linguistics. Mentreal, Canada: ACL, 1998: 1398-1402
24 Small, Steven L. and Garrison W. Cottrell, Parsing and comprehending with word experts. Wendy Lenhert and Martin Ringle, eds., Strategies for Natural Language Processing. Lawrence Erlbaum and Associates, Hillsdale, NJ. 1982: 89-147
25 Eric Brill. Transformation-Based Error-Driving Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging. Computational Linguistics. 1995, 21(4): 543-566
26 Robert A. Amsler, John S. White. Development of a computational methodology for deriving natural language semantic structures via analysis of machine-readable dictionaries, Final report on NSF project MSC77-01315, University of Texas at Austin, Austin, TX, 1979
27 Michael Lesk, Automated Sense Disambiguation Using Machine-readable Dictionaries: How to tell a pine cone from an icee cream cone. In Procedings of the 1986 SIGDOC Conference, Toronto, Canada, June, 1986: 24-26
28 J. Cowie, J. Guthrie, and L. Guthrie. Lexical disambiguation using simulated annealing. In Proceedings of the International Conference on Computational Linguistics. COLING. 1992:359-365
29 D. Yarowsky. Word sense disambiguation using statistical models of Roget‘s categories trained on large corpora. In Proceedings of the International Conference on Computational Linguistics, COLING.1992:454-460
30 G. Grefenstette. Evaluation Techniques for Automatic Semantic Extraction: Comparing Syntatic and Window–based Approaches. In Proceedings of the ACL SIGLEX Workshop on Acquisition of Lexical Knowledge from Text, 1993:143~153
31 Alpha, K, Luk. Statistical sense disambiguation with relatively small corpus using dictionary definitions. In: Proc of 33rd Annual Meeting of the Association for Computational Linguistics. Massachusetts Institute of Technology, Cambridge, Massachusetts, 1995:181~188
32 Agirre, Eneko, German Rigau, A proposal for word sense disambiguation using conceptual distance, Proceedings of the 1st International Conference on Recent Advances in Natural Language Processing, Velingrad, 1995
33 Philip Resnik. Disambiguating noun groupings with respect to WordNet senses. Proceedings of the 3rd Workshop on Very Large Corpora, MIT, 1995:54~68
34 S. Banerjee, T. Pedersen, An adapted Lesk algorithm for word sense disambiguation using WordNet, in: Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City, 2002:136~145
35 R. Mihalcea, P. Tarau, and E. Figa. PageRank on semantic networks, with application to word sense disambiguation. In Proceedings of the 20st International Conference on Computational Linguistics (COLING 2004), Geneva, Switzerland. 2004:1126~1132
36 S. Brin and L. Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7):107–117
37 Ravi Sinha and Rada Mihalcea, Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity, in Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2007), Irvine, CA, September 2007:363~369
38 Abney, Steven P. Statistical methods. In Encyclopedia of Cognitive Science. Nature Publishing Group, Macmillian. 2005:283~293
39 Lucia Specia, Maria das Gracas Volpe Nunes, Gabriela Castelo Branco Ribeiro, and Mark Stevenson. Multilingual versus monolingual WSD. InEACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together. Trento, Italy, April 2006:33~40
40 Specia, L.; Nunes, M.G.V.; Stevenson, M. (2006c). Translation Context Sensitive WSD. 11th Annual Conference of the European Association for Machine Translation, EAMT-2006. Oslo, June 19-20, 2006:227~232
41 Gerard Escudero Bakx. Machine Learning Techniques for Word Sense Disambiguation. Universitat Politμecnica de Catalunya. PhD thesis. Barcelona, May 22, 2006
42 Rada Mihalcea. Word Sense Disambiguation. Encyclopedia of Machine Learning. Springer. 2007
43卢志茂.无指导词义消歧方法研究.哈尔滨工业大学博士学位论文. 2006
44 W. Gale, K. W. Church, and D. Yarowsky. One Sense Per Discourse. In Proceedings of the DARPA Speech and Natural Language Workshop, 1992:233~237
45 C. Leacock, G. Towell, and E. Voorhes. Towards Building Contextual Representations of Word Sense Using Statistical Models. In Proceedings of SIGLEX Workshop on Acquisition of Lexical Knowledge from Text, Association for Computational Linguistics. 1993:10~20
46 T. Pedersen and R. Bruce. A New Supervised Learning Algorithm for Word Sense Disambiguation. In Proceedings of the National Conference on Artificial Intelligence, AAAI, 1997:604~609
47 G. Escudero, L. M`arquez, and G. Rigau. Naive Bayes and Exemplar–Based Approaches to Word Sense Disambiguation Revisited. In Proceedings of the European Conference on Artificial Intelligence, ECAI, 2000:421~425
48 D. Yuret. Some Experiments with a Naive Bayes WSD System. In Proceedings of the International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval–3, 2004:265~268
49 G. Chao and M. G. Dyer. Maximum Entropy Models for Word Sense Disambiguation. In Proceedings of COLING-2002, Taipei, 2002:431~437
50 A. Su′arez and M. Palomar. A Maximum Entropy–based Word Sense Disambiguation System. In Proceedings of the International Conference on computational Linguistics, COLING, 2002:297-313
51 Tanja Gaustad. A Lemma-Based Approach to a Maximum Entropy Word Sense Disambiguation System for Dutch. In Proceedings of COLING-2004, Taipei, 2004:778~784
52 D. Yarowsky. Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL, 1994:88~95
53 R. L. Rivest. Learning Decision Lists. Machine Learning, 2, 1987:229~246
54 D. Yarowsky. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, 1995:189~196
55 David Yarowsky. Hierarchical Decision Lists for Word Sense Disambiguation. Computer and the Humanities, 2000, 34:179~186
56 Black, Ezra. An experiment in computational discrimination of englishword senses. IBM Journal of Research and Development, 1988, Vol. 32(2): 185~194
57 R. J. Mooney. Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, 1996:82~91
58 Ted Pedersen. A Decision Tree of Bigrams is an Accurate Predictor of Word Sense. In Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, 2001:79~86
59 Ted Pedersen. Evaluating the Effectiveness of Ensembles of Decision Trees in Disambiguating Senseval Lexical Samples. In the Proceedings of the Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia, July 11, 2002:81~87
60 H. Sch¨utze. Dimensions of Meaning. In Proceedings of Supercomputing‘92. 1992:787~796
61 Schutze, H. Word space. In Hanson, S. J., Cowan, J. D., & Giles, C. L. Eds., Advances in Neural Information Processing Systems. Morgan Kaufmann Publishers, San Mateo CA.1993:895~902
62 D. Yarowsky, S. Cucerzan, R. Florian, C. Schafer, and R. Wicentowski.The Johns Hopkings Senseval–2 System Descriptions. In Proceedings of the International Workshop on evaluating Word Sense Disambiguation Systems, Senseval–2, 2001:163~166
63 H. T. Ng. Exemplar-Base Word Sense Disambiguation: Some Recent Improvements. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, 1997
64 V. Hoste, A. Kool, and W. Daelemans. Classifier Optimization and Combination in the English All Words Task. In Proceedings of the International Wordshop on Evaluating Word Sense Disambiguation Systems, 2001:84~86
65 B. Decadt, V. Hoste, W. Daelemans, and A. van den Bosch. GAMBL, Genetic Algorithm Optimization of Memory–Based WSD. In Proceedings of the International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval–3, 2004:108~112
66 R. Mihalcea and E. Faruque. SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, 2004:155~158
67 G. Escudero, L. M`arquez, and G. Rigau. A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation. In Proceedings of the Computational Natural Language Learning Workshop, CoNLL, 2000:31~36
68 C. Cabezas, P. Resnik, and J. Stevens. Supervised Sense Tagging using Support Vector Machines. In Proceedings of the International Workshop on Evaluating Word Sense Disambiguation Systems, 2001:59~62
69 M. Murata, M. Utiyama, K. Uchimoto, Q. Ma, and H. Isahara. Japanese Word Sense Disambiguation Using the Simple Bayes and Support Vector Machine Methods. In Proceedings of the International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval–2, 2001:135~138
70 Y. K. Lee and H. T. Ng. An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing,EMNLP, 2002:41~48
71 E. Agirre and D. Mart′inez. The Basque Country University system: English and Basque tasks. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, 2004:44~48
72 G. Escudero, L. M`arquez, and G. Rigau. TALP System for the English Lexical Sample Task. In Proceedings of the 3rd International Workshop an Evaluating Word Sense Disambiguation Systems, Senseval-3, Association for Computational Linguistics. 2004:113~116
73 Y.K.Lee, H.T.Ng, andT.K.Chia. Supervised Word Sense Disambiguation with Support Vector Machines and Multiple Knowledge Sources. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:137~140
74 C. Strapparava, A. Gliozzo, and C. Giuliano. Pattern Abstraction and Term Similarity for Word Sense Disambiguation: IRST at Senseval–3. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:229~234
75 M. Carpuat, W. Su, and D. Wu. Augmenting Ensemble Classification for Word Sense Disambiguation with a Kernel PCA Model. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:88~92
76 C. Grozea. Finding optimal parameter settings for high performance word sense disambiguation. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:125~128
77 M. Popescu. Regularized Least–Squares Classification for Word Sense Disambiguation. In Proceedings of the International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Senseval–3, Association for Computational Linguistics. 2004:209~212
78 A. Gliozzo, C. Giuliano and C. Strapparava. Domain Kernels forWordSense Disambiguation. In Proceedings of the 43th Annual Meeting of the Association for Computational Linguistics (ACL), July, 2005:403~410
79 A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka. Selective Sampling for Example-Based Word Sense Disambiguation. Computational Linguistics, 1998, 24(4):573~597
80 T. Pedersen and R. Bruce. Knowledge Lean Word–Sense Disambiguation. In Proceedings of the Conference of the American Association for Artificial Intelligence, AAAI, 1998:800~805
81 T. Pedersen. A Simple Approach to Building Ensembles of Na?ve Bayesian Classifiers for Word Sense Disambiguation. In Proceedings of the First Annual Meeting of the North American Chapter of the Association for Computational Linguistics, Seattle, WA, May, 2000: 63~69
82 Ted Pedersen. A Decision Tree of Bigrams is an Accurate Predictor of Word Sense. In Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, 2001: 79~86
83 Florian Radu, Silviu Cucerzan, Charles Schafer, and David Yarowsky. Combining classifiers for word sense disambiguation. Natural Language Engineering. 2002, 8(4):327~341
84 Florian Radu and David Yarowsky. Modeling consensus: Classifier combination for word sense disambiguation. In Proceedings of EMNLP-2002. 2002:25~32
85 Dan Klein, KristinaToutanova, H.Tolga Ilhan, Sepandar D. Kamvar and Christopher D. Manning. Combining Heterogeneous Classifiers forWord-Sense Disambiguation. Proceedings of the SIGLEX/SENSEVAL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia, July 2002:74~80
86 G. Escudero, L. M`arquez, and G. Rigau. Boosting Applied to Word Sense Disambiguation. In Proceedings of the European Conference on Machine Learning, ECML, 2000:129~141
87 Brown, Gavin, Jeremy Wyatt, Rachel Harris, and Xin Yao. Diversity creation methods: A survey and categorization. Information Fusion Journal Special issue on Diversity in Multiple Classifier Systems. 2004:5~20
88 G.A. Miller and W.G. Charles. Contextual correlates of semantic similarity.Language and Cognitive Processes, 1991, 6(1):1~28
89 T. Pedersen and R. Bruce. Distinguishing word senses in untagged text. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, Providence, RI, August, 1997:197~207
90 T. Pedersen and R. Bruce. Knowledge lean word sense disambiguation. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, WI, July, 1998: 800~805
91 Henrich. Schütze. Automatic Word Sense Discrimination. Computational Linguistics. 1998, 24(1): 97~123
92 A. Purandare and T. Pedersen. Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces. In Proceedings of the Conference on Computational Natural Language Learning, Boston, MA, 2004:41~48
93 A. Purandare and T. Pedersen. Improving Word Sense Discrimination with Gloss Augmented Feature Vectors. In the Proceedings of the Workshop on Lexical Resources for the Web and Word Sense Disambiguation, Puebla Mexico, November 22, 2004
94 Cheng Niu, Wei Li, et al. Context Clustering for Word Sense Disambiguation Based on Modeling Pairwise Context Similarities. In Proceedings of the 42th ACL, The Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, Association for Computational Linguistics. July 2004:187~190
95 C. Leacock, M. Chodorow, G.A. Miller, Using Corpus Statistics and WordNet Relations for Sense Identification. Computational Linguistics, 1998, 24(1):147~166
96 R. Mihalcea and I. Moldovan. A Method for Word Sense Disambiguation of Unrestricted Text. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics, ACL, 1999:152~158
97 R. Mihalcea and I. Moldovan. An Automatic Method for Generating Sense Tagged Corpora. In Proceedings of the Conference of the American Association for Articial Intelligence, AAAI, 1999:461~466
98 R. Mihalcea. Bootstrapping Large Sense Tagged Corpora. In Proceedings of the International Conference on Languages Resources and Evaluation, LREC, 2002:1407~1411
99 E. Agirre and D. Mart‘inez. Unsupervised WSD based on automatically retrieved examples: The importance of bias. In Proceedings of the International Conference on Empirical Methods in Natural Language Processing, EMNLP, 2004:25~32
100 Xinglong Wang and John Carroll. Word Sense Disambiguation Using Sense Examples Automatically Acquired from a Second Language. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, October 2005:547~554
101 X. Wang and D. Martinez. Word sense disambiguation using automatically translated sense examples. In Proceedings of EACL 2006 Workshop on Cross Language Knowledge Induction, Trento, Italy. 2006:45~52
102 D Martinez, E Agirre, X Wang. Word relatives in context for word sense disambiguation. Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), 2006:42~50
103 I. Dagan, A. Itai and U. Schwall. Two Languages are More Informative than one. Proceedings of the 29th Annual Meeting of the ACL,18-21 Berkeley, California, 1991:130~137
104 I. Dagan and Alon Itai. Word Sense Disambiguation Using a Second Language Monolingual Corpus. Computational Linguistics, 1994, 20(4):56~596
105 G. Kikui. Resolving Translation Ambiguity Using Non-parallel Bilingual Corpora. In Proceedings of ACL‘99 Workshop on Unsupervised Learning in Natural Language Processing, 1999:31~36
106 H. Kaji and Y. Morimoto. Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora. In Proceedings of COLING-2002, Taipei, 2002:155~161
107 Ming Zhou, Yuan Ding, Changning Huang. Improving Translation Selection with a New Translation Model Trained by Independent Monolingual Corpora. Computational Linguistics and Chinese Language Processing, February 2001, Vol. 6(1):1~26
108 HYUN AH LEE, JUNTAE YOON AND GIL CHANG KIM. Translation Selection by Combining Multiple Measures for Sense Disambiguation andWord Selection International Journal of Computer Processing of Oriental Languages, 2003, Vol. 16(3):1~21
109 Philipp Koehn and Kevin Knight. Estimating word translation probabilities from unrelated monolingual corpora using the EM algorithm. In Proceedings of the National Conference on Articial Intelligence, pages, Austin, TX, USA. 2000:711~715
110 N. Ide. Parallel Translations and Sense Discriminators. In Proceedings of the ACLSIGLEX Workshop on Standardizing Lexical Resources, 1999: 52~61
111 Nancy Ide. Cross-lingual Sense Determination: Can it work? In Computers and the Humanities: Special Issue on Senseval, 2000, 34:147~152
112 Nancy Ide, T. Erjavec, et al. Automatic Sense Tagging Using Parallel Corpora. Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium, Tokyo, 2001:83~89
113 Nancy Ide, Tomaz Erjavec, et al. Sense Discrimination with Parallel Corpora. In Proceedings of the ACL SIGLEX Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, 2002:54~60
114 Hwee Tou Ng, Bin Wang, et al. Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study. In Proceedings of the 41th Annual Meeting of the Association for Computational Linguistics, 2003:455~462
115 Mona Diab and Philip Resnik. An Unsupervised Method for Word Sense Tagging Using Parallel Corpora. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002:255~262
116 Mona Diab. Word Sense Disambiguation within a Multilingual Framework. University of Maryland College Park. PhD Thesis, USA, 2003
117 Mona Diab. An Unsupervised Approach for Boot-strapping Arabic Word Sense Tagging. Proceedings of Computational Approaches to Arabic Based Script Languages, COLING 2004. Geneva, Switzerland, 2004:43~50
118 Mona Diab. Relieving the Data Acquisition Bottleneck in Word Sense Disambiguation. In Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics, 2004:303~310
119 Yoshua Bengio and Christopher Kerorvant. Extracting Hidden Sense Probabilities from Bitexts. Technical report, TR 1231, Departementd‘informatique et recherche operationnelle, Universite de Montreal. 2003
120 I. Bhattacharya, L. Getoor, Y. Bengio. Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models. In Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics, 2004:287~294
121 P. Resnik and D. Yarowsky. A Perspective on Word Sense Disambiguation Methods and Their Evaluation. In Proceedings of the ACL SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What, and How? 1997:79~86
122 Franz Josef Och and Hermann Ney. Improved Statistical Alignment Models. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, 2000:440~447
123 Mirella Lapata and Frank Keller. Web-based Models for Natural Language Processing. ACM Transactions on Speech and Language Proccessing, February 2005, Vol. 2(1):1~30
124 A. Kilgarriff. ?Web as Corpus‘, in Proc. Corpus Linguistic Conf. UCREL-Lancaster Univ, UK. 2001:342~344
125 Resnik, P. and Smith, N. A. The web as a parallel corpus. Computational Linguistics. 2003,Vol. 29(3):349~380
126 Chklovski, T., and Mihalcea, R. Building a sense tagged corpus with Open Mind Word Expert. In Proceedings of the ACL 2002 Workshop on‖Word Sense Disambiguation: Recent Successes and Future Directions‖Philadelphia, July 2002:116~122
127 Rada Mihalcea, Using Wikipedia for Automatic Word Sense Disambiguation, in Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL 2007), Rochester, April 2007:196~203
128 Chao, G., and Dyer, M. Word sense disambiguation of adjectives using probabilistic networks. In Proceedings of the 18th International Conference on Computational Linguistics (COLING 2000), Saarbrucken, 2000:152~158
129 Turney, P. Mining the Web for synonyms:PMI-IR versus LSA on TOEFL. Proceedings of the Twelfth European Conference on Machine Learning. Berlin: Springer-Verlag. 2001:491~502
130 Turney, P. Word Sense disambiguation by web mining for word co-occurrence probabilities. In Proc. SENSEVAL-3. Barcelona, Spain, July 2004:239-242
131 Rosso, P., Montes, M., Buscaldi, D., Pancardo, A., and Villase, A. Two Web-based Approaches for Noun Sense Disambiguation. Int. Conf. on Comput. Linguistics and Intelligent Text Processing, CI-CLing-2005, Springer Verlag, LNCS (3406), Mexico D.F., Mexico, 2005:261-273
132 YANG Che-Yu. Word sense disambiguation using semantic relatedness measurement. Journal of Zhejiang University SCIENCE A. 2006, Vol.7 (10):1609~1625
133 Blum, A., and Mitchell, T. Combining labeled and unlabeled data with co-training. In Proceedings of the lth Annual Conference on Computational Learning Theory (COLT-98). 1998:92~100
134 R. Mihalcea and D. I. Moldovan. An Iterative Approach to Word Sense Disambiguation. In Proceedings of FLAIRS-2000, Orlando, FL, May, 2000: 219~223
135 R. Mihalcea. Co-training and Self-training for Word Sense Disambiguation. Proceedings of the European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, August 2004
136 Thanh Phong Pham, Hwee Tou Ng, and Wee Sun Lee. Word Sense Disambiguation with Semi-Supervised Learning. In Proc. of the 20th National Conference on Artificial Intelligence (AAAI 2005), 2005:1093~1098
137 Seong-Bae Pare, Byoung-Tak Zhang, et al. Word Sense Disambiguation by Learning Decision Trees from Unlabeled Data. Kluwer Academic Publishers. Manufactured in The Netherlands. Applied Intelligence, 2003, Vol.19:27~38
138 D. Yarowsky, "One Sense Per Collocation," in Proceedings, ARPA Human Language Technology Workshop, Princeton, 1993:266~271
139 K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proceedings of the Conference on Information and Knowledge Management CIKM-2000, McLean, Virginia. 2000:86~93
140 Cong Li and Hang Li. Word translation disambiguation using bilingualbootstrapping. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002:343-351
141 Zheng-Yu Niu, Dong-Hong Ji and Chew Lim Tan. Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning. In Proceedings of the 43th Annual Meeting of the Association for Computational Linguistics (ACL), July 2005:395~402
142 Zheng-Yu Niu, Dong-Hong Ji, Chew Lim Tan. A semi-supervised feature clustering algorithm with application to word sense disambiguation. Human Language Technology Conference Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Vancouver, British Columbia, Canada, 2005:907~914
143鲁松,白硕,黄雄,张健.基于向量空间模型的有导词义消歧.计算机研究与发展. 2001, Vol. 38(6):662-667
144卢志茂,刘挺,张刚,李生.基于依存分析改进贝叶斯模型的词义消歧.高技术通讯. 2003, Vol. 13(5):1~7
145卢志茂,刘挺,郎君,李生.神经网络和贝叶斯模型在汉语词义消歧上的对比研究.高技术通讯. 2004, Vol. 14(8):15~19
146荀恩东,赵铁军,李生. BT863Ⅱ英汉机器翻译中译文选择方法.情报学报. 1999, Vol. 18(2):99~104
147荀恩东,李生等.基于汉语二元同现的统计词义消歧方法研究.高技术通讯. 1998, Vol. 8(10):21~25
148张晶,李生,姚建民,赵铁军.基于目标语词汇组合合理性评价的译文选择模型.计算机研究与发展. 2003,Vol. 40(9):1361-1367
149黄昌宁,李涓子.词义排歧的一种语言模型.语言文字应用. 2000, 3:85~90
150陈清才,王晓龙.一种基于词矢量的汉语语义量化模型.计算机研究与发展. 2001, Vol. 38(2): 208~212
151鲁松,白硕,黄雄,基于向量空间模型中义项词语的无导词义消歧.软件学报. 2002,Vol. 13(6):1082~1089
152陈浩,何婷婷,姬东鸿.基于k-means聚类的无导词义消歧.中文信息学报. 2005,Vol. 14(9):10-16
153杨尔弘,张国清,张永奎.基于义原同现频率的汉语词义排歧方法.计算机研究与发展. 2001, Vol. 38(7):834~837
154 Zhimao Lu, Haifeng Wang, Jianmin Yao, Ting Liu, Sheng Li, An EquivalentPseudoword Solution to Chinese Word Sense Disambiguation, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL(ACL2006). Sydney, Australia, 2006:457~464
155李涓子,黄昌宁,杨尔弘.一种自组织的汉语词义排歧方法.《中文信息学报》. 1999,Vol. 13(9):1~8
156全昌勤,何婷婷,姬东鸿,刘辉.从搭配知识获取最优种子的词义消歧方法.中文信息学报. 2005,Vol. 19(1):30~35
157 Philip Resnik. Mining the Web for Bilingual Text. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, 1999:527~534
158 Rada Mihalcea, Word Sense Disambiguation Using Pattern Learning and Automatic Feature Selection, in Journal of Natural Language and Engineering (JNLE), December 2002, Vol. 8:343~358
159 A. Montoyo, A. Suarez, G. Rigau, and M. Palomar. Combining knowledge- and corpus-based word sense disambiguation methods. Journal of Artificial Intelligence Research, 2005, Vol. 23:299–330
160 Good, Irwin J. The population frequencies of species and the distribution of population parameters. Biometrika, 1953, Vol. 40(3/4):237-264
161 Brown, Peter F.,Della Pietra, Vincent J.,deSouza, Peter V.,Lai,Jennifer C.and Mercer Robert L.―Class-based ngram models of natural language.‖Computational Linguistics, 1992, Vol. 18(4):467-479
162 Dagan, Ido; Marcus, Shaul; and Markovitch, Shaul. Contextual word similarity and estimation from sparse data. Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, Columbus, Ohio, June 1993: 22-26
163 Peng Jin, Yunfang Wu and Shiwen Yu, 2007. SemEval-2007 Task 5: Multilingual Chinese-English Lexical Sample. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:19~23,
164 Saif Mohammad, Graema Hirst, Philip Resnik. TOR, TORMD:Distribtional Profiles of Concepts for Unsupervised Word Sense Disambiguation. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:326~333
166 William A. Gale, K. W. Church, and D. Yarowsky. Word on Statictical Methods for Word Sense Disambiguation. In Robert Goldman, Peter Norvig. Eugene Charniak, and Bill Gale (eds.). Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, Menlo Park, CA. AAAI Press, 1992:54~60
167 H. Schütze. Context Space. In Robert Goldman, Peter Norvig. Eugene Charniak, and Bill Gale (eds.). Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, Menlo Park, CA. AAAI Press, 1992:113~112
168 Gaustad, Tanja. Statistical corpus-based word sense disambiguation: Pseudowords vs. real ambiguous words. In Companion Volume to the Proceedings of the 39th Annnual Meeting of the Associations for Computational Linguistics (ACL/EACL 2001)– Proceedings of the Student Workshop. Toulouse, France. 2001:61~66
169 G.K.Zipf. Psycho-biology of Language. The MIT Press, 1935
170 David Yarowsky and Radu Florian. Evaluating sense disambiguation across diverse parameter spaces. Nautral Language Engineering. 2002, Vol. 8(4):293~310
171 Agirre, E., Martinez, D. Exploring Automatic Word Sense Disambiguation with Decision Lists and the Web. Proceedings of the COLING Workshop on Semantic Annotation and Intelligent Content. Saarbrticken, Germany. 2000:17~25
172 Gonzalo, J., Verdejo, F., Chugar, I. The Web as a Resource for WSD. In: 1st MEANING Workshop, Spain. 2003
173 Yee Seng Chan and Hwee Tou Ng. Estimating Class Priors in Domain Adaptation for Word Sense Disambiguation. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL. Sydney, July, 2006:89~96,
174 Vinci Liu and James R.Curran. Web Text Corpus for Natural LanguageProcessing. Proceedings of EACL-06, 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, IT, 2006:233~240
175 Frank Keller and Mirella Lapata. Using the web to obtain frequencies for unseen bigrams. Computational Linguistics. 2003, Vol. 29(3):459~484
176 Danushka Bollegala, Yutaka Matsuo Mitsuru lshizuka. Measuring Semantic Similarity between Words Using Web Search Engines. In Proceedings of the Sixteenth International World Wide Web Conference (WWW2007). Banff, Alberta, CANADA. May 8-12, 2007:757~767.
177 Church, K.W., Hanks, P. Word Association Norms, Mutual Information and Lexicography. In: Proceedings of the 27th Annual Conference of the Association of Computational Linguistics. 1989:76~83
178 F. Smadja, K. R. McKeown, and Vasileios Hatzivassiloglou. Translating Collocations for Bilingual Lexicons: A statistical approach. Computational Linguistics. 1996, Vol. 22(1):1~38
179 W. A. Gale, K. W. Church. Identifying Word Correspondences in Parallel Texts.In: Proceedings of the 4th DARPA workshop on Speech and Natual Language. 1991:152~157
180 T. Dunning. Accurate Methods for the Statistics of Surprise and Coincidence. Computational Linguistics. 1993, Vol. 19(1):61~74
181 I. D. Melamed. Automatic Construction of Clean Broad-Coverage Translation Lexicons. In: proceedings of 2nd Conference of the Association for Machine Translation in the Americas. Montreal, Canada. 1996:125~134
182 I. D. Melamed. Empirical Methods for MT Lexicon Development. Proceedings of 3rd Conf. Of Association for Machine Translation in the Americas. 1998:18~30
183 Shudong Huang and David Graff. Chinese-english translation lexicon version 3.0. Linguistic Data Consortium. 2002
184 PengYuan Liu, TieJun Zhao and MuYun Yang. HIT-WSD: Using Search Engine for Multilingual Chinese-English Lexical Sample Task. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:169~172
185 Shinzato, K and Torisawa, K. Acquiring hyponymy relations from web documents. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. Boston, 2004:73-80
186 Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka. Measuring Semantic Similarity between Words Using Web Search Engines. In Proceedings of the Sixteenth International World Wide Web Conference (WWW2007). Banff, Alberta, CANADA. May 8-12, 2007:757~767
187 Ted Pedersen, Satanjeev Banerjee, Siddharth Patwardhan. Maximizing Semantic Relatedness to Perform Word Sense Disambiguation. 2005. Available at http://www.msi.umn.edu/general/Reports/rptfiles/2005-25.pdf. University of Minnesota Supercomputing Institute Research Report UMSI 2005/25, March.
188 G. Miller and W. Charles. Contextual correlates of semantic similarity. Language and Cognitive Processes. 1998, Vol. 6(1):1~28
189 H. Rubenstein and J. Goodenough. Contextual correlates of synonymy. Communications of the ACM. 1965, Vol. 8:627~633
190 M. Sahami and T. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proc. of 15th International World Wide Web Conference.2006:377~386
191 Deniz Yuret. KU: Word Sense Disambiguation by Substitution. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). Prague, June, zech Republic. Association for Computational Linguistics Conference. 2007:207~214,
192 D. Hiemstfa. A Linguistically Motivated Probabilistic Model of Information Retrieval. Proceedings of the 2th European Conference on Research and Advance Technology for Digital Libraries, 1998:569~584
193 Shixin Yu: Feature Selection and Classifier Ensembles: a Study on Hyperspectral Remote Sensing Data. http://142.129.203.3/Visielab/Thesis/Shixin/Thesis_Yu.Pdf. 2003
194 Ted Pedersen, a baseline methodology for word sense disambiguation, in the Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City. February,2002:17~23
195 J.Kittler, M.Hatef, R.P. Duin et al. On Combining Classifiers. IEEE Trans. Patter Anal. Mach. Intell. 1998:226~239
196 L.breiman. Bagging Predictors. Machine Learning, 1996, Vol. 24(2):123~140
197 L. Xu, A. Krzyzak, C.Y.Suen. Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Transactions on Systems.1992:418~435
198陈群秀,现代汉语述语动词机器词典的扩充和槽关系研究,《语言文字应用》,2001 , Vol. (4):98~104
199陈力为,袁琦.《中文信息处理应用平台工程》,电子工业出版社1995年版
200吴蔚天.《汉语计算语义学——关系,关系语义场和形式分析》,电子工业出版社. 1999
201 Qun Liu and Shiwen Yu. TransEasy: A Chinese-English Machine Translation System Based on Hybrid Approach. In David Farwell et al., editors, Machine Translation and the Information Soup. Third Conference of the Association for Machine Translation in Americas (AMTA). Langhorne, PA. USA, October 1998:514~517
202 Copestake A. An approach to building the hierarchical element of a lexical knowledge base from a machine readable dictionary. Proceedings of the First International Workshop on Inheritance in Natural Language Processing. Tilburg. 1990:19~29
203 Riloff E, Shepherd J. A corpus-based approach for building semantic lexicons. Proceedings of the 2nd Conference on Empirical Methods in Matural Language Processing. Rhode Island, USA. 1997:117~124
204 Richardson S., Dolan W., and Vandervende L. MindNet: acquiring and structuring semantic information from text. Proc. of COLING98, Montreal , CA. 1998.1098~1102
205 Craig J. A. McEwan, Iadh Ounis, and Ian Ruthven. Building bilingual dictionaries from parallel web documents. In Proc. of ECIR, 2002:303~323
206 Dorr, B. J., Levow, G.-A., and Lin, D. Building a Chinese-English Mapping between Verb Concepts for Mul-tilingual Applications. In Proceedings of the Fourth Conference of the Association for Machine Translation in theAmericas (AMTA). Cuernavaca, Mexico, 2000:1~12
207 Dorr, B. J. Gina-Anne Levow, Dekang Lin, and Scott Thomas. Chinese-English Semantic Resource Construction, 2nd International Conference on Language Resources and Evaluation (LREC2000), Athens, Greece, 2000:757~760
208张晶,姚建民,赵铁军,李生.基于WordNet和HowNet建设双语语义词典.高技术通讯. 2001, Vol. 11(12):28~32
209 HSIN-HSI CHEN, CHI-CHING LIN, AND WEN-CHENG LIN, Building a Chinese-English WordNet for Translingual Applications, ACM Transactions on Asian Language Information Processing. June 2002, Vol. 1(2):103~122
210 CHEN, H .H. LIN, C. C., AND LIN, W. C. Construction of a Chinese-English WordNet and its application to CLIR. In Proceedings of the Fifth International Workshop on Information Retrieval with Asian Languages (Hong Kong, Sept.-Oct. 2000). ACM Press, New York, NY, 2000:189~196
211 Huang, Chu-Ren. Elanna I. J. Tseng, Dylan B. S. Tsai and Brian Murphy. Cross-lingual Portability of Semantic relations: Bootstrapping Chinese WordNet with English WordNet Relations. Languages and Linguistics. 2003, Vol. 4(3):509~532
212 Jason S. Chang, Tracy Lin, Geeng-Neng You,Thomas C. Chuang, Ching-Ting Hsieh. Building A Chinese WordNet Via Class-Based Translation Model, Computational Linguistics and Chinese Language Processing, August 2003, Vol. 8(2):61~76
213刘群,李素建.基于《知网》的词汇语义相似度计算.第三届汉语词汇语义学研讨会,台北, 2002
214 Miller, G. A, Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. Introduction to WordNet: An on-line Lexical Database.In Five Papers on WordNet, CSL report, Cognitive Science Laboratory, Princeton University. International Journal of Lexicography. 1990, Vol. 3(4):235~244
215姚天顺,张俐,高竹. WordNet综述.语言文字应用. 2001, 1:27~32
216 Resnik P. Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint 1995 Conference on ArtificialIntelligence. Montreal. 1995:448~453,
217 Lin D. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning. Madison, Wisconsin. 1998:296~304
218 Jiang J., Conrath D. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics. Taiwan. 1997:19~33
219 Leacock C., Chodorow M. Combining local context and WordNet similarity for word sense identification. In Christiane Fellbaum, editor, WordNet: A Lexical Reference System and its Application. MIT Press, Cambridge, MA. 1998:265~283
220 Wu Z., Palmer M, Verb Semantics and Lexical Selection, In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico, 1994:133~138
221 Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi, WordNet Similarity-Measuring the Relatedness of Concepts, In Proceedings of AAAI04, Intelligent Systems Demonstration 2004:1024~1025
222杨沐昀,刘晓月,李生.基于汉英双语语料库的汉英词典编撰研究.情报学报, 2003, Vol. 22(3):310~314
223 Carpuat, M. and Ngai, G. et al. Creating a Bilingual Ontology: A Corpus-Based Approach for Aligning WordNet and HowNet. in Proceedings of the 1st Global WordNet Conference(GWC2002), Mysore, India, 2002
224吕雅娟.基于双语语料库对齐的翻译知识自动获取技术研究.哈尔滨工业大学工学博士学位论文, 2003
225 Alexander Budanitsky, Graeme Hirst. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on Wordnet and Other Lexical Resources, Second meeting of the NAACL, Pittsburgh, USA. 2001:29~34