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.
| Original language | English (US) |
|---|---|
| Title of host publication | Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings |
| Publisher | Springer Verlag |
| Pages | 208-222 |
| Number of pages | 15 |
| ISBN (Print) | 3540322051, 9783540322054 |
| DOIs | |
| State | Published - 2006 |
| Event | 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006 - Mexico City, Mexico Duration: Feb 19 2006 → Feb 25 2006 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3878 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006 |
|---|---|
| Country/Territory | Mexico |
| City | Mexico City |
| Period | 2/19/06 → 2/25/06 |
Bibliographical note
Funding Information:We are grateful for general support from the Don T. Nakanishi Award. N. A. Ponce’s work on this study was partially supported by the Robert Wood Johnson Foundation (Advancing the Disaggregation of Ethnic/Racial Data Through Technical Assistance, Training, and Case-Making; grant 76329; primary investigator: N. A. P.). R. C. Chang, N. Pierson, and J. Greer’s work on this study was supported by the University of Chicago, Harris School of a Public Policy, Summer 2020 Computational Analysis and Public Policy Internship Fund.