Abstract
We cast name discrimination as a problem in clustering short contexts. Each occurrence of an ambiguous name is treated independently, and represented using second–order context vectors. We calibrate our approach using a manually annotated collection of five ambiguous names from the Web, and then apply the learned parameter settings to three held-out sets of pseudo-name data that have been reported on in previous publications. We find that significant improvements in the accuracy of name discrimination can be achieved by using very wide bigrams, which are ordered pairs of words with up to 48 intervening words between them. We also show that recent developments in automatic cluster stopping can be used to predict the number of underlying identities without any significant loss of accuracy as compared to previous approaches which have set these values manually.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Artiles, J., Gonzalo, J., Sekine, S.: The SemEval-2007 WePS evaluation: Establishing a benchmark for the web people search task. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval 2007), Prague, Czech Republic, June 2007, pp. 64–69. Association for Computational Linguistics (2007)
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 (1998)
Gale, W., Church, K., Yarowsky, D.: One sense per discourse. In: Proceedings of the Fourth DARPA Speech and Natural Language Workshop (1992)
Gooi, C.H., Allan, J.: Cross-document coreference on a large scale corpus. In: HLT-NAACL 2004: Main Proceedings, Boston, Massachusetts, USA, May 2 - 7, pp. 9–16 (2004)
Kulkarni, A.: Unsupervised context discrimination and automatic cluster stopping. Master’s thesis, University of Minnesota (July 2006)
Kulkarni, A., Pedersen, T.: Name discrimination and email clustering using unsupervised clustering and labeling of similar contexts. In: Proceedings of the Second Indian International Conference on Artificial Intelligence, Pune, India, December 2005, pp. 703–722 (2005)
Landauer, T., Dumais, S.: A solution to Plato’s problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104, 211–240 (1997)
Levin, E., Sharifi, M., Ball, J.: Evaluation of utility of LSA for word sense discrimination. In: Proceedings of the Human Language Technology Conference of the NAACL, New York City, June 2006, pp. 77–80 (2006)
Pedersen, T., Kayaalp, M., Bruce, R.: Significant lexical relationships. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Portland, OR, August 1996, pp. 455–460 (1996)
Pedersen, T., Kulkarni, A.: Selecting the right number of senses based on clustering criterion functions. In: Proceedings of the Posters and Demo Program of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy, April 2006, pp. 111–114 (2006)
Pedersen, T., Kulkarni, A.: Discovering identities in web contexts with unsupervised clustering. In: Proceedings of the IJCAI 2007 Workshop on Analytics for Noisy Unstructed Text Data, Hyderabad, India, January 2007, pp. 23–30 (2007)
Pedersen, T., Kulkarni, A.: Unsupervised discrimination of person names in web contexts. In: Proceedings of the Eighth International Conference on Intelligent Text Processing and Computational Linguistics, February 2007, pp. 299–310 (2007)
Pedersen, T., Kulkarni, A., Angheluta, R., Kozareva, Z., Solorio, T.: An unsupervised language independent method of name discrimination using second order co-occurrence features. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 208–222. Springer, Heidelberg (2006)
Pedersen, T., Purandare, A., Kulkarni, A.: Name discrimination by clustering similar contexts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 226–237. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pedersen, T. (2009). Improved Unsupervised Name Discrimination with Very Wide Bigrams and Automatic Cluster Stopping. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-00382-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00381-3
Online ISBN: 978-3-642-00382-0
eBook Packages: Computer ScienceComputer Science (R0)