Abstract
Previous studies on extracting class attributes from unstructured text consider either Web documents or query logs as the source of textual data. Web search queries have been shown to yield attributes of higher quality. However, since many relevant attributes found in Web documents occur infrequently in query logs, Web documents remain an important source for extraction. In this paper, we introduce Bootstrapped Web Search (BWS) extraction, the first approach to extracting class attributes simultaneously from both sources. Extraction is guided by a small set of seed attributes and does not rely on further domain-specific knowledge. BWS is shown to improve extraction precision and also to improve attribute relevance across 40 test classes.
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References
Tokunaga, K., Kazama, J., Torisawa, K.: Automatic discovery of attribute words from web documents. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS, vol. 3651, pp. 106–118. Springer, Heidelberg (2005)
Paşca, M.: Organizing and searching the World Wide Web of facts - step two: Harnessing the wisdom of the crowds. In: Proceedings of the 16th World Wide Web Conference (WWW 2007), Banff, Canada, pp. 101–110 (2007)
Paşca, M., Van Durme, B., Garera, N.: The role of documents vs. queries in extracting class attributes from text. In: Proceedings of the 16th International Conference on Information and Knowledge Management (CIKM 2007), Lisbon, Portugal, pp. 485–494 (2007)
Lee, L.: Measures of distributional similarity. In: Proceedings of the 37th Annual Meeting of the Association of Computational Linguistics (ACL 1999), College Park, Maryland, pp. 25–32 (1999)
Agichtein, E., Gravano, L.: Snowball: Extracting relations from large plaintext collections. In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL 2000), San Antonio, Texas, pp. 85–94 (2000)
Lin, D., Pantel, P.: Concept discovery from text. In: Proceedings of the 19th International Conference on Computational linguistics (COLING 2002), Taipei, Taiwan, pp. 1–7 (2002)
Shinzato, K., Torisawa, K.: Acquiring hyponymy relations from Web documents. In: Proceedings of the 2004 Human Language Technology Conference (HLT-NAACL 2004), Boston, Massachusetts, pp. 73–80 (2004)
Brants, T.: TnT - a statistical part of speech tagger. In: Proceedings of the 6th Conference on Applied Natural Language Processing (ANLP 2000), Seattle, Washington, pp. 224–231 (2000)
Voorhees, E.: Evaluating answers to definition questions. In: Proceedings of the 2003 Human Language Technology Conference (HLT-NAACL 2003), Edmonton, Canada, pp. 109–111 (2003)
Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., Slattery, S.: Learning to construct knowledge bases from the world wide web. Artificial Intelligence 118, 69–113 (2000)
Schubert, L.: Turing’s dream and the knowledge challenge. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), Boston, Massachusetts (2006)
Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the 1999 Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC 1999), College Park, Maryland, pp. 189–196 (1999)
Shinyama, Y., Sekine, S.: Named entity discovery using comparable news articles. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), Geneva, Switzerland, pp. 848–853 (2004)
Klementiev, A., Roth, D.: Weakly supervised named entity transliteration and discovery from multilingual comparable corpora. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL 2006), Sydney, Australia, pp. 817–824 (2006)
Riloff, E., Jones, R.: Learning dictionaries for information extraction by multi-level bootstrapping. In: Proceedings of the 16th National Conference on Artificial Intelligence (AAAI 1999), Orlando, Florida, pp. 474–479 (1999)
Feldman, R., Rosenfeld, B.: Boosting unsupervised relation extraction by using NER. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP-ACL 2006), Sydney, Australia, pp. 473–481 (2006)
Thelen, M., Riloff, E.: A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, Pennsylvania, pp. 214–221 (2002)
Cafarella, M., Downey, D., Soderland, S., Etzioni, O.: KnowItNow: Fast, scalable information extraction from the Web. In: Proceedings of the Human Language Technology Conference (HLT-EMNLP 2005), Vancouver, Canada, pp. 563–570 (2005)
Chklovski, T., Gil, Y.: An analysis of knowledge collected from volunteer contributors. In: Proceedings of the 20th National Conference on Artificial Intelligence (AAAI 2005), Pittsburgh, Pennsylvania, pp. 564–571 (2005)
Ravi, S., Paşca, M.: Using structured text for large-scale attribute extraction. In: Proceedings of the 17th International Conference on Information and Knowledge Management (CIKM 2008), Napa Valley, California, pp. 1183–1192 (2008)
Yoshinaga, N., Torisawa, K.: Open-domain attribute-value acquisition from semi-structured texts. In: Proceedings of the 6th International Semantic Web Conference (ISWC 2007), Workshop on Text to Knowledge: The Lexicon/Ontology Interface (OntoLex 2007), Busan, South Korea, pp. 55–66 (2007)
Probst, K., Ghani, R., Krema, M., Fano, A., Liu, Y.: Semi-supervised learning of attribute-value pairs from product descriptions. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, pp. 2838–2843 (2007)
Pantel, P., Pennacchiotti, M.: Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL 2006), Sydney, Australia, pp. 113–120 (2006)
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Reisinger, J., Paşca, M. (2009). Low-Cost Supervision for Multiple-Source Attribute Extraction. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_31
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DOI: https://doi.org/10.1007/978-3-642-00382-0_31
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