Abstract
Information extraction, and specifically event and relation extraction from text, is an important problem in the age of big data. Current solutions to these problems require large amounts of training data or extensive feature engineering to find domain-specific events. We introduce a novel Interactive Learning approach that greatly reduces the number of training examples needed and requires no feature engineering. Our method achieves event detection precision in the 80 s and 90 s with only 1 h of human supervision.
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- 1.
The corpus is a collection of articles from mainstream English-language press provided by a news aggregator who wished to remain anonymous.
References
Baker, C.F., Fillmore, C.J., Lowe, J.B.: The berkeley framenet project. In: Proceedings of the COLING-ACL, pp. 86–90 (1998)
Grishman, R., Sundheim, B.: Message understanding conference-6: a brief history. In: Proceedings of the 16th Conference on Computational Linguistics - vol. 1, COLING 1996, pp. 466–471. Association for Computational Linguistics, Stroudsburg (1996)
Soderland, S., Fisher, D., Aseltine, J., Lehnert, W.: Crystal inducing a conceptual dictionary. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 2, pp. 1314–1319. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Ahn, D.: The stages of event extraction. In: Proceedings of the Workshop on Annotating and Reasoning About Time and Events, ARTE 2006, pp. 1–8. Association for Computational Linguistics, Stroudsburg (2006)
Vlachos, A., Buttery, P., Séaghdha, D.Ó., Briscoe, T.: Biomedical event extraction without training data. In: Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task, pp. 37–40. Association for Computational Linguistics, Boulder, June 2009
Grishman, R., Westbrook, D., Meyers, A.: NYU’s English ACE 2005 system description. Technical report, Department of Computer Science, New York University (2005)
Liao, S., Grishman, R.: Filtered ranking for bootstrapping in event extraction. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, pp. 680–688. Association for Computational Linguistics, Stroudsburg (2010)
Aone, C., Ramos-Santacruz, M.: Rees: a large-scale relation and event extraction system. In: Proceedings of the Sixth Conference on Applied Natural Language Processing, pp. 76–83. Association for Computational Linguistics, Seattle, April 2000
Dzendzik, D., Serebryakov, S.: Semi-automatic generation of linear event extraction patterns for free texts. In: SYRCoDIS 2013, pp. 5–9 (2013)
Creswell, C., Beal, M.J., Chen, J., Cornell, T.L., Nilsson, L., Srihari, R.K.: Automatically extracting nominal mentions of events with a bootstrapped probabilistic classifier. In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pp. 168–175. Association for Computational Linguistics, Sydney, July 2006
Xu, F., Uszkoreit, H., Li, H.: Automatic event and relation detection with seeds of varying complexity. In: Proceedings of the 2006 AAAI Workshop on EventExtractionand Synthesis, pp. 12–17 (2006)
Huang, R., Riloff, E.: Bootstrapped training of event extraction classifiers. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2012, pp. 286–295. Association for Computational Linguistics, Stroudsburg, (2012)
Settles, B.: Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 1467–1478. Association for Computational Linguistics, Stroudsburg (2011)
Culotta, A., Kristjansson, T., McCallum, A., Viola, P.: Corrective feedback and persistent learning for information extraction. Artif. Intell. 170(14–15), 1101–1122 (2006)
Jones, R., Ghani, R., Mitchell, T., Rilo, E.: Active learning for information extraction with multiple view feature sets. In: ATEM-2003 (2003)
Altmeyer, R., Grishman, R.: Active Learning of Event Detection Patterns. New York University, New York (2009)
Angeli, G., Tibshirani, J., Wu, J.Y., Manning, C.D.: Combining distant and partial supervision for relation extraction. In: EMNLP (2014)
Wang, R.C., Cohen, W.W.: Language-independent set expansion of named entities using the web. In: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 342–350. IEEE Computer Society, Washington, DC, USA (2007)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, ACL 1994, pp. 133–138. Association for Computational Linguistics, Stroudsburg (1994)
Sarafraz, F., Eales, J., Mohammadi, R., Dickerson, J., Robertson, D., Nenadic, G.: Biomedical event detection using rules, conditional random fields and parse tree distances. In: Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task, pp. 115–118. Association for Computational Linguistics, Boulder, June 2009
Wang, D.Z., Michelakis, E., Franklin, M.J., Garofalakis, M.N., Hellerstein, J.M.: Probabilistic declarative information extraction. In: Li, F. (ed.) ICDE, pp. 173–176. IEEE, Long Beach, CA (2010)
Peng, F., McCallum, A.: Accurate information extraction from research papers using conditional random fields. In: HLT-NAACL 2004, pp. 329–336 (2004)
Wick, M., Singh, S., Kobren, A., McCallum, A.: Assessing confidence of knowledge base content with an experimental study in entity resolution. In: Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, AKBC 2013, pp. 13–18. ACM, New York (2013)
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We would like to thank Jacob Joseph and Eduard Hovy for their valuable advice.
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Tydykov, M., Zeng, M., Gershman, A., Frederking, R. (2015). Interactive Learning with TREE: Teachable Relation and Event Extraction System. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_23
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DOI: https://doi.org/10.1007/978-3-319-19581-0_23
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