creators_name: Nadeau, David creators_name: Turney, Peter D. creators_name: Matwin, Stan type: confposter datestamp: 2006-08-01 lastmod: 2011-03-11 08:56:32 metadata_visibility: show title: Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity ispublished: pub subjects: comp-sci-lang subjects: comp-sci-mach-learn subjects: comp-sci-art-intel full_text_status: public keywords: named entity, unsupervised named entity recognition abstract: In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system’s architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands). date: 2006 date_type: published pagerange: 266-277 refereed: TRUE referencetext: Chinchor, N. (1998) MUC-7 Named Entity Task Definition, version 3.5. Proc. of the Seventh Message Understanding Conference. Cohen, W. and Fan, W. (1999) Learning Page-Independent Heuristics for Extracting Data from Web Page, Proc. of the International World Wide Web Conference. Collins M. and Singer, Y. (1999) Unsupervised Models for Named Entity Classification. Proc. of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S. and Yates, A. (2005) Unsupervised Named-Entity Extraction from the Web: An Experimental Study. Artificial Intelligence, 165, pp. 91-134. Evans, R. (2003) A Framework for Named Entity Recognition in the Open Domain. Proc. Recent Advances in Natural Language Processing. Hearst, M. (1992) Automatic Acquisition of Hyponyms from Large Text Corpora. Proc. of International Conference on Computational Linguistics. Lin, D. and Pantel, P. (2001) Induction of Semantic Classes from Natural Language Text. Proc. of ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Ling, C., and Li, C. (1998). Data Mining for Direct Marketing: Problems and Solutions. Proc. International Conference on Knowledge Discovery and Data Mining. Mikheev, A. (1999) A Knowledge-free Method for Capitalized Word Disambiguation. Proc. Conference of Association for Computational Linguistics. Mikheev, A., Moens, M. and Grover, C. (1999) Named Entity Recognition without Gazetteers. Proc. Conference of European Chapter of the Association for Computational Linguistics. Nadeau, D. (2005) Création de surcouche de documents hypertextes et traitement du langage naturel, Proc. Computational Linguistics in the North-East. Palmer, D. D. and Day, D. S. (1997) A Statistical Profile of the Named Entity Task. Proc. ACL Conference for Applied Natural Language Processing. Petasis, G., Vichot, F., Wolinski, F., Paliouras, G., Karkaletsis, V. and Spyropoulos, C. D. (2001) Using Machine Learning to Maintain Rule-based Named-Entity Recognition and Classification Systems. Proc. Conference of Association for Computational Linguistics. Riloff, E. and Jones, R (1999) Learning Dictionaries for Information Extraction using Multi-level Bootstrapping. Proc. of National Conference on Artificial Intelligence. Sekine, S., Sudo, K., Nobata, C. (2002) Extended Named Entity Hierarchy, Proc. of the Language Resource and Evaluation Conference. Zhu, X., Wu, X. and Chen Q. (2003) Eliminating Class Noise in Large Data-Sets, Proc. of the International Conference on Machine Learning. citation: Nadeau, David and Turney, Peter D. and Matwin, Stan (2006) Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity. [Conference Poster] document_url: http://cogprints.org/5025/1/NRC-48727.pdf