Abstract
Previous work by Pedersen, Purandare and Kulkarni (2005) has resulted in an unsupervised method of name discrimination that represents the context in which an ambiguous name occurs using second order co–occurrence features. These contexts are then clustered in order to identify which are associated with different underlying named entities. It also extracts descriptive and discriminating bigrams from each of the discovered clusters in order to serve as identifying labels. These methods have been shown to perform well with English text, although we believe them to be language independent since they rely on lexical features and use no syntactic features or external knowledge sources. In this paper we apply this methodology in exactly the same way to Bulgarian, English, Romanian, and Spanish corpora. We find that it attains discrimination accuracy that is consistently well above that of a majority classifier, thus providing support for the hypothesis that the method is language independent.
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References
Bagga, A., Baldwin, B.: Entity–based cross–document co–referencing using the vector space model. In: Proceedings of the 17th international conference on Computational linguistics, pp. 79–85. Association for Computational Linguistics (1998)
Gaustad, T.: Statistical corpus-based word sense disambiguation: Pseudowords vs. real ambiguous words. In: Proceedings of the Student Research Workshop at ACL-2001, Toulouse, France, pp. 61–66 (2001)
Ginter, F., Boberg, J., Jrvine, J., Salakoski, T.: New techniques for disambiguation in natural language and their application to biological text. Journal of Machine Learning Research 5, 605–621 (2004)
Gooi, C.H., Allan, J.: Cross-document coreference on a large scale corpus. In: Dumais, S., Marcu, D., Roukos, S. (eds.) HLT-NAACL 2004: Main Proceedings, Boston, Massachusetts, USA, May 2 - May 7, pp. 9–16. Association for Computational Linguistics (2004)
Hatzivassiloglou, V., Duboue, P., Rzhetsky, A.: Disambiguating proteins, genes, and RNA in text: A machine learning approach. In: Proceedings of the 9th International Conference on Intelligent Systems for Molecular Biology, Tivoli Gardens, Denmark (July 2001)
Mann, G., Yarowsky, D.: Unsupervised personal name disambiguation. In: Daelemans, W., Osborne, M. (eds.) Proceedings of CoNLL-2003, Edmonton, Canada, pp. 33–40 (2003)
Nakov, P., Hearst, M.: Category-based pseudowords. In: Companion Volume to the Proceedings of HLT-NAACL 2003 - Short Papers, Edmonton, Alberta, Canada, May 27 - June 1, pp. 67–69 (2003)
Pedersen, T., Purandare, A., Kulkarni, A.: Name discrimination by clustering similar contexts. In: Proceedings of the Sixth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, February 2005, pp. 220–231 (2005)
Purandare, A.: Discriminating among word senses using McQuitty’s similarity analysis. In: Proceedings of the Student Research Workshop at HLT-NAACL 2003, Edmonton, Alberta, Canada, May 27 - June 1, pp. 19–24 (2003)
Purandare, A., Pedersen, T.: Word sense discrimination by clustering contexts in vector and similarity spaces. In: Proceedings of the Conference on Computational Natural Language Learning, Boston, MA, pp. 41–48 (2004)
Schütze, H.: Automatic word sense discrimination. Computational Linguistics 24(1), 97–123 (1998)
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Pedersen, T., Kulkarni, A., Angheluta, R., Kozareva, Z., Solorio, T. (2006). An Unsupervised Language Independent Method of Name Discrimination Using Second Order Co-occurrence Features. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_23
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DOI: https://doi.org/10.1007/11671299_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32205-4
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