Abstract
Most research in Information Extraction concentrates on the extraction of relations from texts but less work has been done about their organization after their extraction. We present in this article a multi-level clustering method to group semantically equivalent relations: a first step groups relation instances with similar expressions to form clusters with high precision; a second step groups these initial clusters into larger semantic clusters using more complex semantic similarities. Experiments demonstrate that our multi-level clustering not only improves the scalability of the method but also improves clustering results by exploiting redundancy in each initial cluster.
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References
Akbik, A., Broß, J.: Extracting semantic relations from natural language text using dependency grammar patterns. In: SemSearch 2009 Workshop (2009)
Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI 2007, pp. 2670–2676 (2007)
Bayardo, R.J., Ma, Y., Srikant, R.: Scaling up all pairs similarity search. In: WWW 2007, pp. 131–140 (2007)
Bollegala, D.T., Matsuo, Y., Ishizuka, M.: Relational Duality: Unsupervised Extraction of. Semantic Relations between Entities on the Web. In: WWW 2010, pp. 151–160 (2010)
Dolan, B., Quirk, C., Brockett, C.: Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources. In: COLING 2004, pp. 350–356 (2004)
Dongen, S.V.: Graph Clustering by Flow Simulation. Ph.D. thesis, University of Utrecht (2000)
Eichler, K., Hemsen, H., Neumann, G.: Unsupervised relation extraction from web documents. In: LREC 2008, pp. 1674–1679 (2008)
Ertöz, L., Steinbach, M., Kumar, V.: A New Shared Nearest Neighbor Clustering Algorithm and its Applications. In: Workshop on Clustering High Dimensional Data and its Applications of SIAM ICDM 2002, pp. 105–115 (2002)
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: EMNLP 2011, pp. 1535–1545 (2011)
Ferret, O.: Testing Semantic Similarity Measures for Extracting Synonyms from a Corpus. In: LREC 2010, pp. 3338–3343 (2010)
Gamallo, P., Garcia, M., Fernández-Lanza, S.: Dependency-Based Open Information Extraction. In: Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, pp. 10–18 (2012)
Hasegawa, T., Sekine, S., Grishman, R.: Discovering relations among named entities from large corpora. In: ACL 2004, pp. 415–422 (2004)
Heylen, K., Peirsmany, Y., Geeraerts, D., Speelman, D.: Modelling Word Similarity: An Evaluation of Automatic Synonymy Extraction Algorithms. In: LREC 2008, pp. 3243–3249 (2008)
Kok, S., Domingos, P.: Extracting semantic networks from text via relational clustering. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 624–639. Springer, Heidelberg (2008)
Lin, D.: An Information-Theoretic Definition of Similarity. In: ICML 1998, pp. 296–304 (1998)
Mausam, S.M., Soderland, S., Bart, R., Etzioni, O.: Open language learning for information extraction. In: EMNLP-CoNLL 2012, pp. 523–534 (2012)
Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI 2006, pp. 775–780 (2006)
Min, B., Shi, S., Grishman, R., Lin, C.Y.: Ensemble Semantics for Large-scale Unsupervised Relation Extraction. In: EMNLP 2012, pp. 1027–1037 (2012)
Moro, A., Navigli, R.: Integrating syntactic and semantic analysis into the open information extraction paradigm. In: IJCAI 2013, pp. 2148–2154 (2013)
Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet:Similarity Measuring the Relatedness of Concepts. In: HLT-NAACL 2004: Demonstrations, pp. 38–41 (2004)
Rink, B., Harabagiu, S.: A generative model for unsupervised discovery of relations and argument classes from clinical texts. In: EMNLP 2011, pp. 519–528 (2011)
Rozenfeld, B., Feldman, R.: High-Performance Unsupervised Relation Extraction from Large Corpora. In: ICDM 2006. pp. 1032–1037 (2006)
Sekine, S.: On-Demand Information Extraction. In: COLING-ACL 2006, pp. 731–738 (2006)
Wang, W., Besançon, R., Ferret, O., Grau, B.: Filtering and Clustering Relations for Unsupervised Information Extraction in Open Domain. In: CIKM 2011, pp. 1405–1414 (2011)
Wang, W., Besançon, R., Ferret, O., Grau, B.: Evaluation of unsupervised information extraction. In: LREC 2012, pp. 552–558 (2012)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: ACL 1994, pp. 133–138 (1994)
Yao, L., Haghighi, A., Riedel, S., McCallum, A.: Structured relation discovery using generative models. In: EMNLP 2011, pp. 1456–1466 (2011)
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Wang, W., Besançon, R., Ferret, O., Grau, B. (2014). Semantic Clustering of Relations between Named Entities. In: Przepiórkowski, A., Ogrodniczuk, M. (eds) Advances in Natural Language Processing. NLP 2014. Lecture Notes in Computer Science(), vol 8686. Springer, Cham. https://doi.org/10.1007/978-3-319-10888-9_36
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DOI: https://doi.org/10.1007/978-3-319-10888-9_36
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