Abstract
When an information extraction system is applied to a new task or domain, we must specify the classes of entities and relations to be extracted. This is best done by a subject matter expert, who may have little training in NLP. To meet this need, we have developed a toolset which is able to analyze a corpus and aid the user in building the specifications of the entity and relation types.
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References
Automatic Content Extraction (ACE), https://www.ldc.upenn.edu/collaborations/past-projects/ace
Akbik, A., Konomi, O., Melnikov, M.: Propminer: A workflow for interactive information extraction and exploration using dependency trees. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: Systems Demonstrations, pp. 157–162 (2013)
Bunescu, R., Mooney, R.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 724–731 (2005)
Freedman, M., Ramshaw, L., Boschee, E., Gabbard, R., Kratkiewicz, G., Ward, N., Weischedel, R.: Extreme Extraction – Machine Reading in a Week. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1437–1446 (2011)
Fu, L., Grishman, R.: An Efficient Active Learning Framework for New Relation Types. In: Proceedings of International Joint Conference on Natural Language Processing (IJCNLP), Nagoya, Japan, pp. 692–698 (2013)
Goldberg, Y., Elhadad, M.: An efficient algorithm for easy-first non-directional dependency parsing. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, pp. 742–750 (2010)
Grishman, R.: Information Extraction: Capabilities and Challenges. The 2012 International Winter School in Language and Speech Technologies, Rovira i Virgili University, Spain (2012), http://cs.nyu.edu/grishman/survey.html
Java Extraction Toolkit, http://cs.nyu.edu/grishman/jet/jet.html
Lehmann, J., Monahan, S., Nezda, L., Jung, A., Shi, Y.: Approaches to Knowledge Base Population at TAC 2010. In: Proceedings of the 2010 Text Analysis Conference (2010)
Li, Y., Chiticariu, L., Yang, H., Reiss, F., Carreno-fuentes, A.: WizIE: A best practices guided development environment for information extraction. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 109–114 (2012)
Min, B., Grishman, R.: Fine-grained entity refinement with user feedback. In: Proceedings of RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition, pp. 2–6 (2011)
Shinyama, Y., Sekine, S.: Preemptive information extraction using unrestricted relation discovery. In: Proceedings of the Human Language Technology Conference of the NAACL, pp. 304–311 (2006)
Sun, A., Grishman, R.: Active learning for relation type extension with local and global data views. In: Proc. 21st ACM International Conf. on Information and Knowledge Management (CIKM 2012), pp. 1105–1112 (2012)
Surdeanu, M., Harabagiu, S.: Infrastructure for Open-Domain Information Extraction. In: Proceedings of the Second International Conference on Human Language Technology Research, HLT 2002, pp. 325–330 (2002)
Tratz, S., Hovy, E.: A fast, effective, non-projective, semantically-enriched parser. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK, pp. 1257–1268 (2011)
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Grishman, R., He, Y. (2014). An Information Extraction Customizer. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2014. Lecture Notes in Computer Science(), vol 8655. Springer, Cham. https://doi.org/10.1007/978-3-319-10816-2_1
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DOI: https://doi.org/10.1007/978-3-319-10816-2_1
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