基于不确定性理论的单词语义相似度度量
|
摘要
当前基于语料库的方法通常受制于所采用的语料库从而难以避免数据稀疏问题，而基于知识的方法虽然简单有效不使用语料库进行训练但多受人的主观意识影响。本文意图探索即简单有效又无需受制于大规模语料库的单词语义相似度度量方法。结合语言的不精确性，本文基于朴素贝叶斯模型、主观Bayes方法、证据理论、确定因子、云模型和模糊集对单词语义相似度进行建模，探讨不确定性理论用于单词语义相似度度量的可行性。利用人工标注样本集采用云模型和模糊集建模部分群体依据单个特征判定语义相似度，然后再将证据合成量化单词语义相似度；并分析了特征模糊化对单词语义相似度的影响。在数据集R&G(65)上，对比算法评判结果与人类评判结果的相关度，其样本Pearson相关系数均高于0.91,比当前最优方法高出至少0.4个百分点，比经典算法高出7~13个百分点；Spearman相关系数均高于0.86，比经典算法高出9~19个百分点。在数据集M&C(30)和WordSim353上也取得了比较好的实验结果。同时本文所提方法的执行效率和经典算法相当。实验结果显示使用不确定性理论量化单词语义相似度是合理有效的，其中利用云模型建模单词语义相似度效果最佳。
Since the birth of computer, teaching it to learn human language has made considerableprogress, and produced a new discipline--Natural Language Processing. It tries to developmodels representing the language skills and language applications, establish computingframeworks to implement such language models, propose corresponding ways to keepimproving these language models, design a variety of practical systems based on these languagemodels, as well as exploring the evaluation techniques of practical systems. The imprecision oflanguage brings challenges for computer language learning, also measuring semantic similaritybetween words has become one of the basic problems of natural language processing.
Measuring semantic similarity between words is a classical and hot problem in naturelanguage processing, the achievement of which has great impact on many applications such asword sense disambiguation, machine translation, ontology mapping, computational linguistics,etc. So far, many approaches have been proposed for word semantic similarity measurementwhich can be grouped into two categories: knowledge-based and corpus-based methods.Corpus-based method is subject to the adopted corpus and cannot avoid data sparsenessproblem, while knowledge-based method is simple, effective, at the same time more intuitive.It does not need corpus for training, but is more impacted byperson’s subjective consciousness.This article attempts to explore methods for word semantic similarity measuring, which aresimple and effective, and are not subject to large-scale corpus. With the imprecision of language,we quantify the word semantic similarity based on Naive Bayes model, Subjective Bayesmethods, Evidence Theory, Certainty factor, Cloud Model and Fuzzy Sets to investigate thefeasibility of uncertainty theory used to measure the word semantic similarity.
The main job of this paper is as follows:(1) Feature Extraction: Based on WordNet, itdefines and quantifies the distance between word pairs and the depth of word pairs with smallamount of computation and high degree of distinguishing characteristics for words’ sense;analyzes the quantifiable of them for word semantic similarity measurement by scatter plot.(2)Defines mean functions by statistics and piecewise linear interpolation technique to describehuman judging word semantic similarity by word pair distance and depth respectively.(3)WordSemantic Similarity Measurement based on Na ve Bayesian Model: to start, Naive Bayesianmodel is given for word semantic similarity measurement; then, generate conditionalprobability distribution based on training data set automatically; after that, obtain posteriorithrough Bayesian inference; at last, quantify word semantic similarity.(4) Word SemanticSimilarity Measurement based on Subjective Bayes Methods: to start, define rules and generatesufficiency measurement of rules; then, obtain comprehensive posteriori by integrating uncertainty reasoning with conclusion uncertainty synthetic strategy; finally, we quantify wordsemantic similarity.(5) Word Semantic Similarity Measurement based on Evidence Theory: tostart, define the identify framework and generate basic probability assignment; then, obtainglobal basic probability assignment by integrating evidence conflict resolution, importancedistribution, and D-S combination rules; finally, we quantify word semantic similarity.(6) WordSemantic Similarity Measurement based on MYCIN Inference Model: define rules and generatecertainty factor of rules; then, obtain integrated certainty factor by evidence combination rules;finally, we quantify word semantic similarity.(7) Word Semantic Similarity Measurement basedon Cloud Model: to start, provide the definition of similar clouds; then, generate similar cloudsby backward cloud generator algorithm and piecewise linear interpolation technique; after that,represent common information and different information with digital features of similar clouds;at last, we quantify word semantic similarity.(8) Word Semantic Similarity Measurement basedon Fuzzy Sets: to start, give the definitions of similarity based on different domains; and then,obtain the membership functions with mean functions; finally, we quantify word semanticsimilarity.(9) Combine Cloud Model with Evidence Theory and MYCIN Inference Model forword semantic similarity measurement.(10) Use Cloud Model and MYCIN Inference Modelto quantify word semantic similarity based on the feature fuzzy processing.
On benchmark data set R&G(65), the sample pearson correlation between our testingresults and human judgments is higher than0.91, with at least0.4%improvements over theexisting best practice,7%~13%improvements over classical methods; Spearman correlationbetween our methods and human judgments is higher than0.86, with9%~19%improvementsover the classical methods. On data set M&C(30) and WordSim353, the experimental results ofour methods are also good. And the computational complexity of our methods is as efficient asclassical methods. The above results indicate that applying uncertainty theory to measure wordsemantic similarity is reasonable and effective, and of which the method of using cloud modelis the best choice.
The innovation of this paper:(1) unlike the current methods that are put forward bydepending on the expert experience, the methods in this paper model human judgement on wordsemantic similarity by word pair distance and depth respectively with manual annotation dataset by Cloud Model and Fuzzy Set, after that, synthesis evidences to quantify word semanticsimilarity.(2) In this paper, uncertainty theory is fully applied to word semantic similarity, andwe put forward the effectiveness analysis of the uncertainty theory used to word semanticsimilarity respectively. SIM-NB, SIM-SB, SIM-DS, SIM-FS, SIM-CL, SIM-CF, SIM-DS(CL),SIM-CF(CL), SIM-CL(FFS) and SIM-CF(CL-FFS) can fuse word pair distance and depth. Thericher the evidence mining, the more complete the training set, and then the more perfect thedata dictionary, consequently the more close to the human word semantic similarity decisionprocess.(3) Analyse the effectiveness of feature fuzzy processing for word semantic similaritymeasurement, try to study the multilayer imprecision of the process of word semantic similaritymeasurement by human being, including similarity uncertainty, similarity judgement uncertainty with single evidence, and feature fuzziness.
The theoretical value of this paper is proposing an innovative method to solve wordsemantic similarity measurement based on uncentainty theory compared with the existingmethods. The practical value is that word sense disambiguation, ontology mapping andontology matching are expected to be enchanced, for our methods are of low time complexitybut effective.
Future outlook:(1) Obtain membership functions of feature fuzzy sets through the training.(2) Consider how to better model human judgement on word semantic similarity by word pairdistance and depth respectively.(3) Explore the features which can be used on word sensesimilarity computation.
Since the birth of computer, teaching it to learn human language has made considerableprogress, and produced a new discipline--Natural Language Processing. It tries to developmodels representing the language skills and language applications, establish computingframeworks to implement such language models, propose corresponding ways to keepimproving these language models, design a variety of practical systems based on these languagemodels, as well as exploring the evaluation techniques of practical systems. The imprecision oflanguage brings challenges for computer language learning, also measuring semantic similaritybetween words has become one of the basic problems of natural language processing.
Measuring semantic similarity between words is a classical and hot problem in naturelanguage processing, the achievement of which has great impact on many applications such asword sense disambiguation, machine translation, ontology mapping, computational linguistics,etc. So far, many approaches have been proposed for word semantic similarity measurementwhich can be grouped into two categories: knowledge-based and corpus-based methods.Corpus-based method is subject to the adopted corpus and cannot avoid data sparsenessproblem, while knowledge-based method is simple, effective, at the same time more intuitive.It does not need corpus for training, but is more impacted byperson’s subjective consciousness.This article attempts to explore methods for word semantic similarity measuring, which aresimple and effective, and are not subject to large-scale corpus. With the imprecision of language,we quantify the word semantic similarity based on Naive Bayes model, Subjective Bayesmethods, Evidence Theory, Certainty factor, Cloud Model and Fuzzy Sets to investigate thefeasibility of uncertainty theory used to measure the word semantic similarity.
The main job of this paper is as follows:(1) Feature Extraction: Based on WordNet, itdefines and quantifies the distance between word pairs and the depth of word pairs with smallamount of computation and high degree of distinguishing characteristics for words’ sense;analyzes the quantifiable of them for word semantic similarity measurement by scatter plot.(2)Defines mean functions by statistics and piecewise linear interpolation technique to describehuman judging word semantic similarity by word pair distance and depth respectively.(3)WordSemantic Similarity Measurement based on Na ve Bayesian Model: to start, Naive Bayesianmodel is given for word semantic similarity measurement; then, generate conditionalprobability distribution based on training data set automatically; after that, obtain posteriorithrough Bayesian inference; at last, quantify word semantic similarity.(4) Word SemanticSimilarity Measurement based on Subjective Bayes Methods: to start, define rules and generatesufficiency measurement of rules; then, obtain comprehensive posteriori by integrating uncertainty reasoning with conclusion uncertainty synthetic strategy; finally, we quantify wordsemantic similarity.(5) Word Semantic Similarity Measurement based on Evidence Theory: tostart, define the identify framework and generate basic probability assignment; then, obtainglobal basic probability assignment by integrating evidence conflict resolution, importancedistribution, and D-S combination rules; finally, we quantify word semantic similarity.(6) WordSemantic Similarity Measurement based on MYCIN Inference Model: define rules and generatecertainty factor of rules; then, obtain integrated certainty factor by evidence combination rules;finally, we quantify word semantic similarity.(7) Word Semantic Similarity Measurement basedon Cloud Model: to start, provide the definition of similar clouds; then, generate similar cloudsby backward cloud generator algorithm and piecewise linear interpolation technique; after that,represent common information and different information with digital features of similar clouds;at last, we quantify word semantic similarity.(8) Word Semantic Similarity Measurement basedon Fuzzy Sets: to start, give the definitions of similarity based on different domains; and then,obtain the membership functions with mean functions; finally, we quantify word semanticsimilarity.(9) Combine Cloud Model with Evidence Theory and MYCIN Inference Model forword semantic similarity measurement.(10) Use Cloud Model and MYCIN Inference Modelto quantify word semantic similarity based on the feature fuzzy processing.
On benchmark data set R&G(65), the sample pearson correlation between our testingresults and human judgments is higher than0.91, with at least0.4%improvements over theexisting best practice,7%~13%improvements over classical methods; Spearman correlationbetween our methods and human judgments is higher than0.86, with9%~19%improvementsover the classical methods. On data set M&C(30) and WordSim353, the experimental results ofour methods are also good. And the computational complexity of our methods is as efficient asclassical methods. The above results indicate that applying uncertainty theory to measure wordsemantic similarity is reasonable and effective, and of which the method of using cloud modelis the best choice.
The innovation of this paper:(1) unlike the current methods that are put forward bydepending on the expert experience, the methods in this paper model human judgement on wordsemantic similarity by word pair distance and depth respectively with manual annotation dataset by Cloud Model and Fuzzy Set, after that, synthesis evidences to quantify word semanticsimilarity.(2) In this paper, uncertainty theory is fully applied to word semantic similarity, andwe put forward the effectiveness analysis of the uncertainty theory used to word semanticsimilarity respectively. SIM-NB, SIM-SB, SIM-DS, SIM-FS, SIM-CL, SIM-CF, SIM-DS(CL),SIM-CF(CL), SIM-CL(FFS) and SIM-CF(CL-FFS) can fuse word pair distance and depth. Thericher the evidence mining, the more complete the training set, and then the more perfect thedata dictionary, consequently the more close to the human word semantic similarity decisionprocess.(3) Analyse the effectiveness of feature fuzzy processing for word semantic similaritymeasurement, try to study the multilayer imprecision of the process of word semantic similaritymeasurement by human being, including similarity uncertainty, similarity judgement uncertainty with single evidence, and feature fuzziness.
The theoretical value of this paper is proposing an innovative method to solve wordsemantic similarity measurement based on uncentainty theory compared with the existingmethods. The practical value is that word sense disambiguation, ontology mapping andontology matching are expected to be enchanced, for our methods are of low time complexitybut effective.
Future outlook:(1) Obtain membership functions of feature fuzzy sets through the training.(2) Consider how to better model human judgement on word semantic similarity by word pairdistance and depth respectively.(3) Explore the features which can be used on word sensesimilarity computation.
引文
[1]冯志伟.自然语言处理的学科定位[J].解放军外国语学院学报.2005,28(3):1-8.
[2]邵艳秋.计算语言学及其近义术语详解[J].术语标准化与信息技术.2009,3:24-27.
[3] Claudia Leacock, Martin Chodorow. Combining local context and WordNet similarity forword sense identification[M]. Cambridge, Britain: MITPress,1998:265-283.
[4] Zhou Ming, Ding Yuan, Huang Changning. Improving Translation Selection with a NewTranslation Model Trained by Independent Monolingual Corpora[J]. ComputationalLinguistics and Chinese Language Processing.2001,6(1):1-26.
[5] Hany Hassan, Ahmed Hassan, Ossama Emam. Unsupervised Information ExtractionApproach Using Graph Mutual Reinforcement[C]. In Proceedings of the2006Conferenceon Empirical Methods in Natural Language Processing. Syndney: Australia,2006,501-508.
[6]吴旭东.主客观结合的语义相似度算法及其应用研究[D].南京邮电大学.2013.
[7]李昊迪.语义相似度混合计算方法[D].哈尔滨工业大学.2013.
[8] http://wordnet.princeton.edu/
[9] http://research.microsoft.com/en-us/projects/mindnet/
[10] https://framenet.icsi.berkeley.edu/fndrupal/
[11] Roy. Rada, Hafedh. Mili, Ellen. Bicknell, Maria Blettner. Development and Application ofa Metric on Semantic Nets[J]. IEEE Transactions on Systems, Man, and Cybernetics.1989,19(1):17-30.
[12]刘青磊.汉语词语及句子相似度算法研究与应用[D].电子科技大学.2011.
[13] Philip Resnik. Using Information Content to Evaluate Semantic Similarity in aTaxonomy[C]. In Proceedings of International Joint Conference for Artificial Intelligence.Montreal: Canada,1995,448-453.
[14] Wu Zhibiao, Martha. Palmer. Verbs Semantics and Lexical Selection[C]. In Proceedingsof the32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces:New Mexico,1994,133-138.
[15] Eneko Agirre, German. Rigau. A Proposal for Word Sense Disambiguation UsingConceptual Distance[C]. In Proceedings of1st International Conference on RecentAdvances in Natural Language Processing. Bulgaria,1995,35-43.
[16] Jay J. Jiang, David W. Conrath. Semantic Similarity Based on Corpus Statistics andLexical Taxonomy[C]. In Proceedings of International Conference on Research inComputational Linguistics. Taiwan,1997,1-15.
[17] Dekang Lin. An Information-theoretic Definition of Similarity[C]. In Proceedings of the15th International Conference on Machine Learning. San Francisco, CA: USA,1998,296-304.
[18] Graeme Hirst, David St-Onge. Lexical Chains as Representations of Context for theDetection and Correction of Malapropisms[M]. Cambridge, Britain: MIT Press,1998:305-332.
[19] Li Yuhua, Zuhair A. Bandar, David McLean. An Approach for Measuring SemanticSimilarity between Words Using Multiple Information Sources[J]. IEEE Transactions onKnowledge and Data Engineering.2003,15(4):871-882.
[20] Yang Dongqiang, David M.W. Powers. Measuring Semantic Similarity in the Taxonomyof WordNet[C]. In Proceedings of the28th Australasian Computer Science Conference.Newcastle, Australia,2005,315-322.
[21] Alexander Budanitsky, Graeme Hirst. Evaluating WordNet-based Measures of LexicalSemantic Relatedness[J]. Computational Linguistics.2006,32(1):13-47.
[22]李昊迪.语义相似度的混合计算方法[D].哈尔滨工业大学.2013.
[23]郭勇.基于《知网》的词语相似度计算研究及应用[D].湖南大学.2012.
[24]张凯勇.基于WordNet的词语及短文本语义相似度算法研究[D].吉林大学.2011.
[25]李文庆.基于医学领域本体的语义相似度算法研究[D].太原理工大学.2013.
[26]陈杰,蒋祖华.领域本体的概念相似度计算[J].计算机工程与应用.2006,33:163-166.
[27] Strube M, Ponzetto S P. WikiRelate! Computing semantic relatedness using Wikipedia[C].In Proceedings of the National Conference on Artificial Intelligence. AAAI Press,2006,21(2):1419-1425.
[28] Milne D. Computing semantic relatedness using wikipedia link structure[C]. InProceedings of the New Zealand computer science research student conference.2007,17-21.
[29] Kim J W, Candan K S. Cp/cv: concept similarity mining without frequency informationfrom domain describing taxonomies[C]. In Proceedings of the15th ACM InternationalConference on Information and Knowledge Management. New York: USA, ACM Press,2006,483-492.
[30] Marco A. Alvarez, SeungJin Lim. A Graph Modeling of Semantic Similarity betweenWords[C]. In Proceedings of International Conference on Semantic Computing. Irvine,California: USA,2007,355-362.
[31] Wan S, Angryk R A. Measuring semantic similarity using wordnet-based contextvectors[C]. IEEE International Conference on Systems, Man and Cybernetics,2007,908-913.
[32] F. M. Couto, M. J. Silva, and p. M. Coutinho. Measuring semantic similarity between geneontology terms[J]. Data and Knowledge Engineering.2007,61(1):137-152.
[33] T. pedersen, S. V. S. pakhomov, S. patwardhan, and C.G.Chute. Measures of semanticsimilarity and relatedness in the biomedical domain[J]. Journal of Biomedical Informatics.2007,40(3):288-299.
[34]曹泽文,钱杰,张维明,邓苏.一种综合的概念相似度计算方法[J].计算机科学.2007,34(3):174-176.
[35]曹泽文,陆昌辉,张维明,邓苏.基于RFCA的概念相似度计算方法[J].计算机科学.2007,34(5):177-180.
[36]王家琴,李仁发,李仲生,唐剑波.一种基于本体的概念语义相似度方法的研究[J].计算机工程.2007,33(11):201-203.
[37]夏天.汉语词语语义相似度计算研究[J].计算机工程.2007,33(6):191-194.
[38] Zhou Z, Wang Y, Gu J. A new model of information content for semantic similarity inWordNet[C]. In Proceedings of Second international conference on future generationcommunication and networking symposia. Sanya: Hainan Island, IEEE Computer Society,2008,9-85.
[39]智慧来,智东杰,刘宗田.基于概念格的概念相似度计算[J].计算机科学.2008,35(9):156-158.
[40]吴开贵,万红波,朱郑州.一种基于语义的本体概念相似度的计算方法[J].计算机科学.2008,35(5):123-125.
[41]黄宏斌,董发花,邓苏,张维明.一种跨本体的语义相似度计算方法[J].计算机科学.2008,35(7):153-157.
[42]智东杰,智慧来,刘宗田. OWL中的概念相似度计算和应用[J].计算机科学.2008,35(12):200-202.
[43] Shi B, Yan J Z, Wang P, et al. Ontology-based measure of semantic similarity betweenconcepts[J]. Computer Engineering,2009,35(19):83-85.
[44] Agirre E, Alfonseca E, Hall K, et al. A study on similarity and relatedness usingdistributional and wordnet-based approaches[C]. In Proceedings of Human LanguageTechnologies: The2009Annual Conference of the North American Chapter of theAssociation for Computational Linguistics. Association for Computational Linguistics,2009,19-27.
[45]史斌,闫健卓,王普,方丽英.基于本体的概念语义相似度度量[J].计算机工程.2009,35(19):83-85.
[46] Ahsaee M G, Naghibzadeh M, Yasrebi S E. Using WordNet to determine semanticsimilarity of words[C]. In Proceedings of the5th International Symposium onTelecommunications. Tehran: IEEE,2010,1019-1027.
[47]徐茜,彭进业,李展.本体映射中一种综合的概念相似度计算方法[J].计算机工程与应用.2010,46(24):34-36.
[48]夏红科,郑雪峰,胡祥.多策略概念相似度计算方法LMSW[J].计算机工程与应用.2010,46(20):33-36.
[49]张帆,钟金宏,黄玲.改进的领域本体概念相似度计算方法[J].计算机工程.2010,36(23):66-68.
[50]魏韡,向阳,陈千.基于基因本体的语义相似度研究[J].计算机工程.2010,36(20):209-211.
[51]吕林涛,董迎.基于上下文的概念语义相似度计算模型[J].计算机工程.2010,36(21):59-61.
[52] Cai Songmei, Lu Zhao. An Improved Semantic Similarity Measure for Word Pairs[C]. InProceedings of2010International Conference on e-Education, e-Business, e-Managementand e-Learning. Sanya: China,2010,212-216.
[53] Qin Peng, Lu Zhao, Yan Yu, Wu Fang. A New Measure of Word Semantic Similarity basedon WordNet Hierarchy and DAG Theory[C]. In Proceedings of International Conferenceon Web Information Systems and Mining. Shanghai: China.2009,181-185.
[54] Giuseppe Pirrò. A Semantic Similarity Metric Combining Features and IntrinsicInformation Content[J]. Data&Knowledge Engineering.2009,68(11):1289-1308.
[55] Resnik P. Semantic Similarity in a Taxonomy: An Information-Based Measure and itsApplication to Problems of Ambiguity in Natural Language[J]. Journal of ArtificialIntelligence Research,2011,11:95-130.
[56]李荣,杨冬,刘磊.基于本体的概念相似度计算方法研究[J].计算机研究与发展.2011,48(Suppl):312-317.
[57]王孝满,闫晶晶.非ISA关系在本体概念相似度计算中的度量方法研究[J].计算机科学.2011,38(7):250-254.
[58]盛志超,陶晓鹏.基于维基百科的语义相似度计算方法[J].计算机工程.2011,37(7):193-195.
[59] David Sánchez, Montserrat Batet, David Isern. Ontology-based Information ContentComputation[J]. Knowledge-Based Systems.2011,24(2):297-303.
[60] David Sánchez, Montserrat Batet, David Isern, A da Valls. Ontology-based SemanticSimilarity: A New Feature-based Approach[J]. Expert Systems with Applications.2012,39(9):7718-7728.
[61] Liu Hongzhe, Bao Hong, Xu De. Concept Vector for Semantic Similarity and Relatednessbased on WordNet Structure[J]. Journal of Systems and Software.2012,85(2):370-381.
[62]齐玉东,闫晓斌,谢晓方.基于LISA理论的概念模型相似度计算[J].计算机工程与应用.2012,48(3):40-42.
[63]甘明鑫,窦雪,王道平,江瑞.一种综合加权的本体概念语义相似度计算方法[J].计算机工程与应用.2012,48(17):148-153.
[64]吴旭东,成卫青,黄卫东.改进的主客观结合的词语语义相似度算法[J].计算机技术与发展.2012,22(9):45-49.
[65]兰美辉,任友俊,徐坚,高炜. k-部排序本体相似度计算[J].计算机应用.2012,32(4):1094-1096.
[66]张玉芳,徐安龙.改进的基因术语间语义相似度计算方法[J].计算机应用.2012,32(5):1329-1331,1354.
[67]张瑞霞,杨国增,吴慧欣.基于《知网》的汉语未登录词语义相似度计算[J].中文信息学报,2012,26(1):16-21.
[68]张仰森,钟鼎.基于SKCC与统计相结合的词语相似度计算方法[J].北京信息科技大学学报(自然科学版).2012,27(6):8-12.
[69]王涛,陈红英.基于贝叶斯网络的概念间语义相似度计算[J].计算机与现代化.2012,9:66-133.
[70]李文清,孙新,张常有,冯烨.一种本体概念的语义相似度计算方法[J].自动化学报.2012,38(2):229-235.
[71]李兆龙,杜军平.一种基于旅游领域本体的概念语义相似度计算模型[C].第三十一届中国控制会议.2012,3863-3868.
[72]唐雅媛,徐德智,赖雅.基于概念特征的语义相似度计算方法[J].计算机工程.2012,38(12):170-172.
[73]李文庆,谢红薇.基于医疗本体的语义相似度评估方法[J].计算机工程与设计.2013,34(4):1287-1291.
[74]罗俊丽,王亚,路凯.基于模糊语义的本体概念相似度计算算法[J].微电子学与计算机.2013,30(7):128-132.
[75]王桐,王磊,吴吉义,徐贺. WordNet中的综合概念语义相似度计算方法[J].北京邮电大学学报.2013,36(2):98-102.
[76]姚佳岷,杨思春.改进的本体概念相似度计算模型[J].计算机应用.2013,33(6):1579-1581,1586.
[77]张瑾.基于《中图法》的语义本体相似度技术研究[J].计算机应用.2013,31(8):71-76.
[78]杨方颖,蒋正翔,张姗姗.基于本体结构的语义相似度计算[J].计算机技术与发展.2013,23(7):52-56.
[79]詹志建,梁丽娜,杨小平.基于百度百科的词语相似度计算[J].计算机科学.2013,40(6):199-202.
[80] Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, Robert L. Mercer. WordSense Disambiguation Using Statistical Methods[C].In Proceedings of the29thannualmeeting on Association for Computational Linguistics.1991,264-270.
[81] Lillian Lee. Similarity-based Approaches to Natural Language Processing[D]. Cambridge,MA: Harvard University,1997.
[82] Ido Dagan, Lillian Lee, Fernando C. N. Pereira. Similarity-based Models of WordCooccurrence Probabilities[J]. Machine Learning.1999,34(1-3):135-142.
[83]胡俊峰,俞士汶.唐宋诗中词汇语义相似度的统计分析及应用[J].中文信息学报,2002,16(4):39-44.
[84] Maguitman A G, Menczer F, Erdinc F, et al. Algorithmic Computation and Approximationof Semantic Similarity[J]. World Wide Web.2006,9(4):431-456.
[85] Turney P D. Similarity of semantic relations[J]. Computational Linguistics,2006,32(3):379-416.
[86] Patwardhan S, Pedersen T. Using WordNet-based context vectors to estimate the semanticrelatedness of concepts[C]. In Proceeding of the EACL2006, workshop on making senseof sense: bring computational linguistics and psycholinguistics together. Trento: Italy,2006,1-8.
[87]章志凌,虞立群,陈奕秋,罗海飞,邵晓敏.基于Corpus库的词语相似度计算方法[J].计算机应用.2006,26(3):638-644.
[88] Gabrilovich E, Markovitch S. Computing semantic relatedness using wikipedia-basedexplicit semantic analysis[C]. In Proceedings of the20th international joint conference onartificial intelligence.2007,6-12.
[89] Cilibrasi R L, Vitanyi P M B. The Google Similarity Distance. IEEE Transactions onknowledge and Data Engineering,2007,19(3):370-383
[90] Islam A, Inkpen D. Semantic text Similarity using corpus-based word similarity and stringsimilarity[J]. ACM Transactions on Knowledge Discovery from Data,2008,2(2):1-25.
[91] Bollegala D, Matsuo Y, Ishizuka M. WebSim: a web-based semantic similarity measure[C].In The21st annual conference of the Japanese society for artificial intelligence (JSAI2007).Miyazaki: Japan;2007,757-766.
[92] Bollegala D, Matsuo Y, Ishizuka M. Measuring semantic similarity between words usingweb search engines[C]. In Proceedings of World Wide Web. NY: USA,2007,757-766.
[93] Bollegala D, Matsuo Y, Ishizuka M. A web search engine-based approach to measuresemantic similarity between words[J]. IEEE Transactions on Knowledge and DataEngineering,2011,23(7):977-990.
[94] Liu Lei, Zhong Maoshang, Lu Ruzhan. Measuring Word Similarity Based on PatternVector Space Model[C]. In Proceedings of International Conference on ArtificialIntelligence and Computational Intelligence. Shanghai: China,2009,72-76.
[95] Xu Tao, Qu Weiguang, Tang Xuri, Ding Dexin, Li Bin, Li Hui. Computing Word Similarityon Large-Scale Corpus[C]. In Proceedings of20094th International Conference onInnovative Computing, Information and Control. Kaohsiung: China.2009,1076-1079.
[96] Kira Radinsky, Eugene Agichten, Evgeniy Gabrilovich, Shaul Markovitch. A Word at aTime: Computing Word Relatedness using Temporal Semantic Analysis[C]. InProceedings of the20th international conference on World Wide Web. Hyderabad: India.2011,337-346.
[97]石静,吴云芳,邱立坤,吕学强.基于大规模语料库的汉语词义相似度计算方法[J].中文信息学报.2013,27(1):1-6.
[98]费洪晓,莫天池,林青,杨艳群,谭叶清,严星俊.基于树状语料库的中文短语相似度计算[J].计算机应用与软件.2013,30(8):19-22.
[99]王石,曹存根,裴亚军,夏飞.一种基于搭配的中文词汇语义相似度计算方法[J].中文信息学报.2013,27(1):7-14.
[100]郭海娟.基于机器学习的蛋白质折叠模式预测研究[D].苏州大学.2010.
[101]丁雷.基于概率建模图像标注算法的研究及实现[D].北京交通大学.2010.
[102] WordNet Website: http://wordnet.princeton.edu/.
[103] H. Rubenstein, J.B. Goodenough. Contextual Correlates of Synonymy[J].Communications of the ACM.1965,8(10):627-633.
[104]倪星.中国廉政方略变迁及其效果分析[J].政治学研究.2011,5.
[105]宋鉴.基于C4.5算法的BBS检索排名策略[D].北京邮电大学.2009.
[106]张师超,严小卫,王成名,聂文龙.不确定性推理技术[M].广西师范大学出版社.1995:26-42.
[107] Shafer G. A Mathematical Theory of Evidence [M]. Princeton: Princeton University Press,1976:12-30.
[108]周皓,李少洪. GDOP引出的冲突证据组合新算法[J].控制与决策.2010,25(2):278-281.
[109]杨风暴.证据冲突的衡量方法研究[J].华北工学院学报.2005,26(1):58-60.
[110]张山鹰,潘泉,张洪才.一种新的证据推理组合规则[J].控制与决策.2000,15(5):540-545.
[111]李德毅,杜鷁.不确定性人工智能[M].北京:国防工业出版社,2005,23-40.
[112]王崇.基于云模型的B2B电子商务信任度评价方法研究[D].华中师范大学.2013.
[113]朱传明.云概念相似性度量及其在数字水印中的应用研究[D].西南交通大学.2013.
[114]牛杰,李众,戴艳.云模型理论在网络课程满意度评价模型构建中的应用[J].中国远程教育.2013,17:36-39.
[115]王国胤,李德毅,姚一豫,梁吉业,苗夺谦,张燕平,张清华.云模型与粒计算[M].北京:科学出版社,2012,6-12.
[116]夏玉成.灰色系统理论及其在地学研究中的意义[J].西北地质.1989,1:48-51.
[2]邵艳秋.计算语言学及其近义术语详解[J].术语标准化与信息技术.2009,3:24-27.
[3] Claudia Leacock, Martin Chodorow. Combining local context and WordNet similarity forword sense identification[M]. Cambridge, Britain: MITPress,1998:265-283.
[4] Zhou Ming, Ding Yuan, Huang Changning. Improving Translation Selection with a NewTranslation Model Trained by Independent Monolingual Corpora[J]. ComputationalLinguistics and Chinese Language Processing.2001,6(1):1-26.
[5] Hany Hassan, Ahmed Hassan, Ossama Emam. Unsupervised Information ExtractionApproach Using Graph Mutual Reinforcement[C]. In Proceedings of the2006Conferenceon Empirical Methods in Natural Language Processing. Syndney: Australia,2006,501-508.
[6]吴旭东.主客观结合的语义相似度算法及其应用研究[D].南京邮电大学.2013.
[7]李昊迪.语义相似度混合计算方法[D].哈尔滨工业大学.2013.
[8] http://wordnet.princeton.edu/
[9] http://research.microsoft.com/en-us/projects/mindnet/
[10] https://framenet.icsi.berkeley.edu/fndrupal/
[11] Roy. Rada, Hafedh. Mili, Ellen. Bicknell, Maria Blettner. Development and Application ofa Metric on Semantic Nets[J]. IEEE Transactions on Systems, Man, and Cybernetics.1989,19(1):17-30.
[12]刘青磊.汉语词语及句子相似度算法研究与应用[D].电子科技大学.2011.
[13] Philip Resnik. Using Information Content to Evaluate Semantic Similarity in aTaxonomy[C]. In Proceedings of International Joint Conference for Artificial Intelligence.Montreal: Canada,1995,448-453.
[14] Wu Zhibiao, Martha. Palmer. Verbs Semantics and Lexical Selection[C]. In Proceedingsof the32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces:New Mexico,1994,133-138.
[15] Eneko Agirre, German. Rigau. A Proposal for Word Sense Disambiguation UsingConceptual Distance[C]. In Proceedings of1st International Conference on RecentAdvances in Natural Language Processing. Bulgaria,1995,35-43.
[16] Jay J. Jiang, David W. Conrath. Semantic Similarity Based on Corpus Statistics andLexical Taxonomy[C]. In Proceedings of International Conference on Research inComputational Linguistics. Taiwan,1997,1-15.
[17] Dekang Lin. An Information-theoretic Definition of Similarity[C]. In Proceedings of the15th International Conference on Machine Learning. San Francisco, CA: USA,1998,296-304.
[18] Graeme Hirst, David St-Onge. Lexical Chains as Representations of Context for theDetection and Correction of Malapropisms[M]. Cambridge, Britain: MIT Press,1998:305-332.
[19] Li Yuhua, Zuhair A. Bandar, David McLean. An Approach for Measuring SemanticSimilarity between Words Using Multiple Information Sources[J]. IEEE Transactions onKnowledge and Data Engineering.2003,15(4):871-882.
[20] Yang Dongqiang, David M.W. Powers. Measuring Semantic Similarity in the Taxonomyof WordNet[C]. In Proceedings of the28th Australasian Computer Science Conference.Newcastle, Australia,2005,315-322.
[21] Alexander Budanitsky, Graeme Hirst. Evaluating WordNet-based Measures of LexicalSemantic Relatedness[J]. Computational Linguistics.2006,32(1):13-47.
[22]李昊迪.语义相似度的混合计算方法[D].哈尔滨工业大学.2013.
[23]郭勇.基于《知网》的词语相似度计算研究及应用[D].湖南大学.2012.
[24]张凯勇.基于WordNet的词语及短文本语义相似度算法研究[D].吉林大学.2011.
[25]李文庆.基于医学领域本体的语义相似度算法研究[D].太原理工大学.2013.
[26]陈杰,蒋祖华.领域本体的概念相似度计算[J].计算机工程与应用.2006,33:163-166.
[27] Strube M, Ponzetto S P. WikiRelate! Computing semantic relatedness using Wikipedia[C].In Proceedings of the National Conference on Artificial Intelligence. AAAI Press,2006,21(2):1419-1425.
[28] Milne D. Computing semantic relatedness using wikipedia link structure[C]. InProceedings of the New Zealand computer science research student conference.2007,17-21.
[29] Kim J W, Candan K S. Cp/cv: concept similarity mining without frequency informationfrom domain describing taxonomies[C]. In Proceedings of the15th ACM InternationalConference on Information and Knowledge Management. New York: USA, ACM Press,2006,483-492.
[30] Marco A. Alvarez, SeungJin Lim. A Graph Modeling of Semantic Similarity betweenWords[C]. In Proceedings of International Conference on Semantic Computing. Irvine,California: USA,2007,355-362.
[31] Wan S, Angryk R A. Measuring semantic similarity using wordnet-based contextvectors[C]. IEEE International Conference on Systems, Man and Cybernetics,2007,908-913.
[32] F. M. Couto, M. J. Silva, and p. M. Coutinho. Measuring semantic similarity between geneontology terms[J]. Data and Knowledge Engineering.2007,61(1):137-152.
[33] T. pedersen, S. V. S. pakhomov, S. patwardhan, and C.G.Chute. Measures of semanticsimilarity and relatedness in the biomedical domain[J]. Journal of Biomedical Informatics.2007,40(3):288-299.
[34]曹泽文,钱杰,张维明,邓苏.一种综合的概念相似度计算方法[J].计算机科学.2007,34(3):174-176.
[35]曹泽文,陆昌辉,张维明,邓苏.基于RFCA的概念相似度计算方法[J].计算机科学.2007,34(5):177-180.
[36]王家琴,李仁发,李仲生,唐剑波.一种基于本体的概念语义相似度方法的研究[J].计算机工程.2007,33(11):201-203.
[37]夏天.汉语词语语义相似度计算研究[J].计算机工程.2007,33(6):191-194.
[38] Zhou Z, Wang Y, Gu J. A new model of information content for semantic similarity inWordNet[C]. In Proceedings of Second international conference on future generationcommunication and networking symposia. Sanya: Hainan Island, IEEE Computer Society,2008,9-85.
[39]智慧来,智东杰,刘宗田.基于概念格的概念相似度计算[J].计算机科学.2008,35(9):156-158.
[40]吴开贵,万红波,朱郑州.一种基于语义的本体概念相似度的计算方法[J].计算机科学.2008,35(5):123-125.
[41]黄宏斌,董发花,邓苏,张维明.一种跨本体的语义相似度计算方法[J].计算机科学.2008,35(7):153-157.
[42]智东杰,智慧来,刘宗田. OWL中的概念相似度计算和应用[J].计算机科学.2008,35(12):200-202.
[43] Shi B, Yan J Z, Wang P, et al. Ontology-based measure of semantic similarity betweenconcepts[J]. Computer Engineering,2009,35(19):83-85.
[44] Agirre E, Alfonseca E, Hall K, et al. A study on similarity and relatedness usingdistributional and wordnet-based approaches[C]. In Proceedings of Human LanguageTechnologies: The2009Annual Conference of the North American Chapter of theAssociation for Computational Linguistics. Association for Computational Linguistics,2009,19-27.
[45]史斌,闫健卓,王普,方丽英.基于本体的概念语义相似度度量[J].计算机工程.2009,35(19):83-85.
[46] Ahsaee M G, Naghibzadeh M, Yasrebi S E. Using WordNet to determine semanticsimilarity of words[C]. In Proceedings of the5th International Symposium onTelecommunications. Tehran: IEEE,2010,1019-1027.
[47]徐茜,彭进业,李展.本体映射中一种综合的概念相似度计算方法[J].计算机工程与应用.2010,46(24):34-36.
[48]夏红科,郑雪峰,胡祥.多策略概念相似度计算方法LMSW[J].计算机工程与应用.2010,46(20):33-36.
[49]张帆,钟金宏,黄玲.改进的领域本体概念相似度计算方法[J].计算机工程.2010,36(23):66-68.
[50]魏韡,向阳,陈千.基于基因本体的语义相似度研究[J].计算机工程.2010,36(20):209-211.
[51]吕林涛,董迎.基于上下文的概念语义相似度计算模型[J].计算机工程.2010,36(21):59-61.
[52] Cai Songmei, Lu Zhao. An Improved Semantic Similarity Measure for Word Pairs[C]. InProceedings of2010International Conference on e-Education, e-Business, e-Managementand e-Learning. Sanya: China,2010,212-216.
[53] Qin Peng, Lu Zhao, Yan Yu, Wu Fang. A New Measure of Word Semantic Similarity basedon WordNet Hierarchy and DAG Theory[C]. In Proceedings of International Conferenceon Web Information Systems and Mining. Shanghai: China.2009,181-185.
[54] Giuseppe Pirrò. A Semantic Similarity Metric Combining Features and IntrinsicInformation Content[J]. Data&Knowledge Engineering.2009,68(11):1289-1308.
[55] Resnik P. Semantic Similarity in a Taxonomy: An Information-Based Measure and itsApplication to Problems of Ambiguity in Natural Language[J]. Journal of ArtificialIntelligence Research,2011,11:95-130.
[56]李荣,杨冬,刘磊.基于本体的概念相似度计算方法研究[J].计算机研究与发展.2011,48(Suppl):312-317.
[57]王孝满,闫晶晶.非ISA关系在本体概念相似度计算中的度量方法研究[J].计算机科学.2011,38(7):250-254.
[58]盛志超,陶晓鹏.基于维基百科的语义相似度计算方法[J].计算机工程.2011,37(7):193-195.
[59] David Sánchez, Montserrat Batet, David Isern. Ontology-based Information ContentComputation[J]. Knowledge-Based Systems.2011,24(2):297-303.
[60] David Sánchez, Montserrat Batet, David Isern, A da Valls. Ontology-based SemanticSimilarity: A New Feature-based Approach[J]. Expert Systems with Applications.2012,39(9):7718-7728.
[61] Liu Hongzhe, Bao Hong, Xu De. Concept Vector for Semantic Similarity and Relatednessbased on WordNet Structure[J]. Journal of Systems and Software.2012,85(2):370-381.
[62]齐玉东,闫晓斌,谢晓方.基于LISA理论的概念模型相似度计算[J].计算机工程与应用.2012,48(3):40-42.
[63]甘明鑫,窦雪,王道平,江瑞.一种综合加权的本体概念语义相似度计算方法[J].计算机工程与应用.2012,48(17):148-153.
[64]吴旭东,成卫青,黄卫东.改进的主客观结合的词语语义相似度算法[J].计算机技术与发展.2012,22(9):45-49.
[65]兰美辉,任友俊,徐坚,高炜. k-部排序本体相似度计算[J].计算机应用.2012,32(4):1094-1096.
[66]张玉芳,徐安龙.改进的基因术语间语义相似度计算方法[J].计算机应用.2012,32(5):1329-1331,1354.
[67]张瑞霞,杨国增,吴慧欣.基于《知网》的汉语未登录词语义相似度计算[J].中文信息学报,2012,26(1):16-21.
[68]张仰森,钟鼎.基于SKCC与统计相结合的词语相似度计算方法[J].北京信息科技大学学报(自然科学版).2012,27(6):8-12.
[69]王涛,陈红英.基于贝叶斯网络的概念间语义相似度计算[J].计算机与现代化.2012,9:66-133.
[70]李文清,孙新,张常有,冯烨.一种本体概念的语义相似度计算方法[J].自动化学报.2012,38(2):229-235.
[71]李兆龙,杜军平.一种基于旅游领域本体的概念语义相似度计算模型[C].第三十一届中国控制会议.2012,3863-3868.
[72]唐雅媛,徐德智,赖雅.基于概念特征的语义相似度计算方法[J].计算机工程.2012,38(12):170-172.
[73]李文庆,谢红薇.基于医疗本体的语义相似度评估方法[J].计算机工程与设计.2013,34(4):1287-1291.
[74]罗俊丽,王亚,路凯.基于模糊语义的本体概念相似度计算算法[J].微电子学与计算机.2013,30(7):128-132.
[75]王桐,王磊,吴吉义,徐贺. WordNet中的综合概念语义相似度计算方法[J].北京邮电大学学报.2013,36(2):98-102.
[76]姚佳岷,杨思春.改进的本体概念相似度计算模型[J].计算机应用.2013,33(6):1579-1581,1586.
[77]张瑾.基于《中图法》的语义本体相似度技术研究[J].计算机应用.2013,31(8):71-76.
[78]杨方颖,蒋正翔,张姗姗.基于本体结构的语义相似度计算[J].计算机技术与发展.2013,23(7):52-56.
[79]詹志建,梁丽娜,杨小平.基于百度百科的词语相似度计算[J].计算机科学.2013,40(6):199-202.
[80] Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, Robert L. Mercer. WordSense Disambiguation Using Statistical Methods[C].In Proceedings of the29thannualmeeting on Association for Computational Linguistics.1991,264-270.
[81] Lillian Lee. Similarity-based Approaches to Natural Language Processing[D]. Cambridge,MA: Harvard University,1997.
[82] Ido Dagan, Lillian Lee, Fernando C. N. Pereira. Similarity-based Models of WordCooccurrence Probabilities[J]. Machine Learning.1999,34(1-3):135-142.
[83]胡俊峰,俞士汶.唐宋诗中词汇语义相似度的统计分析及应用[J].中文信息学报,2002,16(4):39-44.
[84] Maguitman A G, Menczer F, Erdinc F, et al. Algorithmic Computation and Approximationof Semantic Similarity[J]. World Wide Web.2006,9(4):431-456.
[85] Turney P D. Similarity of semantic relations[J]. Computational Linguistics,2006,32(3):379-416.
[86] Patwardhan S, Pedersen T. Using WordNet-based context vectors to estimate the semanticrelatedness of concepts[C]. In Proceeding of the EACL2006, workshop on making senseof sense: bring computational linguistics and psycholinguistics together. Trento: Italy,2006,1-8.
[87]章志凌,虞立群,陈奕秋,罗海飞,邵晓敏.基于Corpus库的词语相似度计算方法[J].计算机应用.2006,26(3):638-644.
[88] Gabrilovich E, Markovitch S. Computing semantic relatedness using wikipedia-basedexplicit semantic analysis[C]. In Proceedings of the20th international joint conference onartificial intelligence.2007,6-12.
[89] Cilibrasi R L, Vitanyi P M B. The Google Similarity Distance. IEEE Transactions onknowledge and Data Engineering,2007,19(3):370-383
[90] Islam A, Inkpen D. Semantic text Similarity using corpus-based word similarity and stringsimilarity[J]. ACM Transactions on Knowledge Discovery from Data,2008,2(2):1-25.
[91] Bollegala D, Matsuo Y, Ishizuka M. WebSim: a web-based semantic similarity measure[C].In The21st annual conference of the Japanese society for artificial intelligence (JSAI2007).Miyazaki: Japan;2007,757-766.
[92] Bollegala D, Matsuo Y, Ishizuka M. Measuring semantic similarity between words usingweb search engines[C]. In Proceedings of World Wide Web. NY: USA,2007,757-766.
[93] Bollegala D, Matsuo Y, Ishizuka M. A web search engine-based approach to measuresemantic similarity between words[J]. IEEE Transactions on Knowledge and DataEngineering,2011,23(7):977-990.
[94] Liu Lei, Zhong Maoshang, Lu Ruzhan. Measuring Word Similarity Based on PatternVector Space Model[C]. In Proceedings of International Conference on ArtificialIntelligence and Computational Intelligence. Shanghai: China,2009,72-76.
[95] Xu Tao, Qu Weiguang, Tang Xuri, Ding Dexin, Li Bin, Li Hui. Computing Word Similarityon Large-Scale Corpus[C]. In Proceedings of20094th International Conference onInnovative Computing, Information and Control. Kaohsiung: China.2009,1076-1079.
[96] Kira Radinsky, Eugene Agichten, Evgeniy Gabrilovich, Shaul Markovitch. A Word at aTime: Computing Word Relatedness using Temporal Semantic Analysis[C]. InProceedings of the20th international conference on World Wide Web. Hyderabad: India.2011,337-346.
[97]石静,吴云芳,邱立坤,吕学强.基于大规模语料库的汉语词义相似度计算方法[J].中文信息学报.2013,27(1):1-6.
[98]费洪晓,莫天池,林青,杨艳群,谭叶清,严星俊.基于树状语料库的中文短语相似度计算[J].计算机应用与软件.2013,30(8):19-22.
[99]王石,曹存根,裴亚军,夏飞.一种基于搭配的中文词汇语义相似度计算方法[J].中文信息学报.2013,27(1):7-14.
[100]郭海娟.基于机器学习的蛋白质折叠模式预测研究[D].苏州大学.2010.
[101]丁雷.基于概率建模图像标注算法的研究及实现[D].北京交通大学.2010.
[102] WordNet Website: http://wordnet.princeton.edu/.
[103] H. Rubenstein, J.B. Goodenough. Contextual Correlates of Synonymy[J].Communications of the ACM.1965,8(10):627-633.
[104]倪星.中国廉政方略变迁及其效果分析[J].政治学研究.2011,5.
[105]宋鉴.基于C4.5算法的BBS检索排名策略[D].北京邮电大学.2009.
[106]张师超,严小卫,王成名,聂文龙.不确定性推理技术[M].广西师范大学出版社.1995:26-42.
[107] Shafer G. A Mathematical Theory of Evidence [M]. Princeton: Princeton University Press,1976:12-30.
[108]周皓,李少洪. GDOP引出的冲突证据组合新算法[J].控制与决策.2010,25(2):278-281.
[109]杨风暴.证据冲突的衡量方法研究[J].华北工学院学报.2005,26(1):58-60.
[110]张山鹰,潘泉,张洪才.一种新的证据推理组合规则[J].控制与决策.2000,15(5):540-545.
[111]李德毅,杜鷁.不确定性人工智能[M].北京:国防工业出版社,2005,23-40.
[112]王崇.基于云模型的B2B电子商务信任度评价方法研究[D].华中师范大学.2013.
[113]朱传明.云概念相似性度量及其在数字水印中的应用研究[D].西南交通大学.2013.
[114]牛杰,李众,戴艳.云模型理论在网络课程满意度评价模型构建中的应用[J].中国远程教育.2013,17:36-39.
[115]王国胤,李德毅,姚一豫,梁吉业,苗夺谦,张燕平,张清华.云模型与粒计算[M].北京:科学出版社,2012,6-12.
[116]夏玉成.灰色系统理论及其在地学研究中的意义[J].西北地质.1989,1:48-51.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |