Abstract
The paper describes a methodology for bootstrapping relation extraction from unstructured text in the context of GATE, but also applied to the KIM semantic annotation platform. The focus is on identifying a set of relations between entities previously found by named entity recognizer. The methodology is developed and applied to three kinds of relations and evaluated both with the ANNIE system and the default information extraction module of KIM. The methodology covers the problem of identifying the task, the target domain, the development of training and testing corpora, and useful lexical resources, the choice of a particular relation extraction approach. The application of information extraction for the Semantic Web also brings a new interesting dimension of not merely recognizing the entity type, but going into instantiation of entity references and linking them to an entity instance in a semantic repository.
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References
Aswani, N., Tablan, V., Bontcheva, K., Cunningham, H.: Indexing and Querying Linguistic Metadata and Document Content. In: RANLP 2005, Borovets, Bulgaria, September 21-23 (2005)
Iria, J., Ciravegna, F.: Relation Extraction for Mining the Semantic Web. In: Proceedings Machine Learning for the Semantic Web Dagstuhl, Seminar 05071, Dagstuhl, DE
Zhao, S., Grishman, R.: Extracting Relations with Integrated Information Using Kernel Methods. In: ACL 2005 (2005)
Agichtein, E.: Scaling Information Extraction to Large Document Collections. IEEE Data Engineering Bulletin Special Issue on Searching and Mining Digital Libraries (December 2005)
Grishman, R.: Adaptive Information Extraction and Sublanguage Analysis. In: Proceedings of the Workshop on Adaptive Text Extraction and Mining at the 17 International Joint Conference on Artificial Intelligence (2001)
Grishman, R.: Information extraction: Techniques and challenges. In: Pazienza, M.T. (ed.) SCIE 1997. LNCS, vol. 1299. Springer, Heidelberg (1997)
GATE User guide, http://gate.ac.uk/sale/tao/index.html
Zelenko, D., Aone, C., Richardella, A.: Kernel Methods for Relation Extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)
Claudio, G., Lavelli, A., Romano, L.: Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), Trento, Italy, April 3-7 (2006)
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Tchalakova, M., Popov, B., Yankova, M. (2006). Methodology for Bootstrapping Relation Extraction for the Semantic Web. In: Euzenat, J., Domingue, J. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2006. Lecture Notes in Computer Science(), vol 4183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861461_24
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DOI: https://doi.org/10.1007/11861461_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40930-4
Online ISBN: 978-3-540-40931-1
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