Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-29T08:51:28.093Z Has data issue: false hasContentIssue false

Exploiting the Wikipedia structure in local and global classification of taxonomic relations*

Published online by Cambridge University Press:  14 March 2012

QUANG XUAN DO
Affiliation:
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA emails: [email protected],[email protected]
DAN ROTH
Affiliation:
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA emails: [email protected],[email protected]

Abstract

Determining whether two terms have an ancestor relation (e.g. Toyota Camry and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in Natural Language Processing applications such as Question Answering, Summarization, and Textual Entailment. Significant work has been done on developing knowledge sources that could support these tasks, but these resources usually suffer from low coverage, noise, and are inflexible when dealing with ambiguous and general terms that may not appear in any stationary resource, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a hierarchical structure of concepts and relations, we describe an algorithmic approach that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint-based inference process that leverages an existing knowledge base to enforce relational constraints among terms and thus improves the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon the existing well-known knowledge sources.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abad, A., Bentivogli, L., Dagan, I., Giampiccolo, D., Mirkin, S., Pianta, E., and Stern, A. 2010. A resource for investigating the impact of anaphora and coreference on inference. In Calzolari, N. (Conference Chair), Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., and Tapias, D. (eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10). Valletta, Malta: European Language Resources Association (ELRA).Google Scholar
Banko, M., Cafarella, M., Soderland, M., Broadhead, M., and Etzioni, O. 2007. Open information extraction from the web. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January, pp. 2670–76.Google Scholar
Banko, M., and Etzioni, O. 2008. The tradeoffs between open and traditional relation extraction. In Proceedings of ACL-08: HLT, pp. 2836. Columbus, OH, USA: Association for Computational Linguistics.Google Scholar
Baroni, M., and Lenci, A. 2010. Distributional memory: a general framework for corpus-based semantics. Computational Linguistics 36: 673721.CrossRefGoogle Scholar
Bishop, C. M. 1996. Neural Networks for Pattern Recognition. Oxford, UK: Oxford University Press.Google Scholar
Chakrabarti, S., Dom, B., Agrawal, R., and Raghavan, P. 1997. Using taxonomy, discriminants, and signatures for navigating in text databases. In Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB '97), pp. 446–55. San Francisco, CA, USA: Morgan Kaufmann.Google Scholar
Chang, M., Ratinov, L., and Roth, D. 2008 (July). Constraints as prior knowledge. In ICML Workshop on Prior Knowledge for Text and Language Processing, pp. 3239.Google Scholar
Dagan, I., Glickman, O., and Magnini, B. (eds.) 2006. The PASCAL Recognising Textual Entailment Challenge, vol. 3944. Berlin, Germany: Springer-Verlag.CrossRefGoogle Scholar
Davidov, D., and Rappoport, A. 2008. Unsupervised discovery of generic relationships using pattern clusters and its evaluation by automatically generated SAT analogy questions. In Proceedings of ACL-08: HLT, pp. 692700. Columbus, OH, USA: Association for Computational Linguistics.Google Scholar
Denis, P., and Baldridge, J. 2007. Joint determination of anaphoricity and coreference resolution using integer programming. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, pp. 236–43. Rochester, New York, USA: Association for Computational Linguistics.Google Scholar
Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA, USA: MIT Press.CrossRefGoogle Scholar
Freund, Y., and Schapire, R. E. 1999. Large margin classification using the perceptron algorithm. Machine Learning 37 (3): 277–96.CrossRefGoogle Scholar
Gabrilovich, E., and Markovitch, S. 2007. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1606–11.Google Scholar
Hearst, M. A. 1992. Automatic acquisition of hyponyms from large text corpora. In Proceedings the International Conference on Computational Linguistics (COLING), pp. 539–45.Google Scholar
Hotho, A., Staab, S., and Gerd, S. 2003. Ontologies improve text document clustering. In IEEE International Conference on Data Mining(ICDM '03), pp. 541544. Washington, DC, USA: IEEE Computer Society.CrossRefGoogle Scholar
Kozareva, Z., Riloff, E., and Hovy, E. 2008. Semantic class learning from the web with hyponym pattern linkage graphs. In Proceedings of ACL-08: HLT, pp. 1048–56. Columbus, OH, USA: Association for Computational Linguistics.Google Scholar
MacCartney, B., and Manning, C. D. 2008. Modeling semantic containment and exclusion in natural language inference. In Proceedings of the 22nd International Conference on Computational Linguistics (COLING '08), vol. 1, pp. 521–28. Stroudsburg, PA, USA: Association for Computational Linguistics.CrossRefGoogle Scholar
Mihalcea, R., and Csomai, A. 2007. Wikify!: linking documents to encyclopedic knowledge. In Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp. 233–42.Google Scholar
Milne, D., and Witten, I. H. 2008. Learning to link with Wikipedia. In Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp. 509–18.Google Scholar
Padó, S., and Lapata, M. 2007. Dependency-based construction of semantic space models. Computational Linguistics 33(June): 161–99.CrossRefGoogle Scholar
Pantel, P., and Pennacchiotti, M. 2006. Espresso: leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pp. 113–20.Google Scholar
Paşca, M. 2007. Organizing and searching the World Wide Web of facts ” step two: harnessing the wisdom of the crowds. In Proceedings of the 16th International Conference on World Wide Web (WWW '07), pp. 101–10. New York, NY, USA: ACM.CrossRefGoogle Scholar
Paşca, M., and Van Durme, B. 2008. Weakly supervised acquisition of open-domain classes and class attributes from web documents and query logs. In Proceedings of ACL-08: HLT, pp. 1927. Columbus, OH, USA: Association for Computational Linguistics.Google Scholar
Ponzetto, S. P., and Strube, M. 2007. Deriving a large scale taxonomy from Wikipedia. In Proceedings of the 22nd National Conference on Artificial Intelligence, vol. 2, pp. 1440–5. Palo Alto, CA, USA: AAAI Press.Google Scholar
Punyakanok, V., Roth, D., and Yih, W. 2008. The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics 34 (2): 257–87.CrossRefGoogle Scholar
Ratinov, L., Downey, D., Anderson, M., and Roth, D. 2011. Local and global algorithms for disambiguation to Wikipedia. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Portland, OR, USA.Google Scholar
Rizzolo, N., and Roth, D. 2010. Learning based Java for rapid development of NLP systems. In Proceedings of the International Conference on Language Resources and Evaluation, Valletta (Malta), May 1723.Google Scholar
Roth, D., and Yih, W. 2004. A linear programming formulation for global inference in natural language tasks. In Ng, H. T. and Riloff, E. (eds.), Proceedings of the Annual Conference on Computational Natural Language Learning (CoNLL), pp. 18. Columbus, OH, USA: Association for Computational Linguistics.Google Scholar
Sammons, M., Vydiswaran, V. G. V., and Roth, D. 2010. “Ask not what textual entailment can do for you. . .”. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, July 11—16, pp. 1199–208. Stroudsburg, PA, USA: Association for Computational Linguistics.Google Scholar
Sarmento, L., Jijkuon, V., de Rijke, M., and Oliveira, E. 2007. “More like these”: growing entity classes from seeds. In Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp. 959–62.Google Scholar
Saxena, A. K., Sambhu, G. V., Kaushik, S., and Subramaniam, L. V. 2007. IITD-IBMIRL system for question answering using pattern matching, semantic type and semantic category recognition. In Proceedings of the Sixteenth Text REtrieval Conference (TREC 2007), Gaithersburg, MD, USA, November 5–9.Google Scholar
Sekine, S. 2006. On-demand information extraction. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Sydney, Australia, July 1721, pp. 731–8.Google Scholar
Snow, R., Jurafsky, D., and Ng, A. Y. 2005. Learning syntactic patterns for automatic hypernym discovery. Advances in Neural Information Processing Systems 17: 12971304.Google Scholar
Snow, R., Jurafsky, D., and Ng, A. Y. 2006. Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (ACL-44), pp. 801–8. Stroudsburg, PA, USA: Association for Computational Linguistics.Google Scholar
Suchanek, F. M., Kasneci, G., and Weikum, G. 2007. Yago: a core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web (WWW '07), pp. 697706. New York, NY, USA: ACM.CrossRefGoogle Scholar
Turney, P. D., and Pantel, P. 2010. From frequency to meaning: vector space models of semantics. Journal of AI Research 37: 141.Google Scholar
Vikas, O., Meshram, A. K., Meena, G., and Gupta, A. 2008 (June). Multiple document summarization using principal component analysis incorporating semantic vector space model. Computational Linguistics and Chinese Language Processing 13: 141–56.Google Scholar
Vyas, V., and Pantel, P. 2009. Semi-automatic entity set refinement. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, (NAACL '09), pp. 290–8. Stroudsburg, PA, USA: Association for Computational Linguistics.Google Scholar