Overview:
A given entity - representing a person, a location, or an organization - may be mentioned in text in multiple, ambiguous ways. Understanding natural language and supporting intelligent access to textual information require identifying whether different mentions of a name, within and across documents, represents the same entity. We demonstrate a browsing tool that incorporates some of our newly developed Machine Learning based technologies in this area. I enables users to trace different mentions of the same entity, presented in different textual forms, across documents.
Relevant Publications:
- A. Doan, X. Li, and D. Roth, MEDIATE: Learning to match entity mentions across text and data bases. (2006)
- X. Li, P. Morie, and D. Roth, Semantic Integration in Text: From Ambiguous Names to Identifiable Entities. AI Magazine. Special Issue on Semantic Integration (2005) pp. 45--68
- W. Shen, X. Li, and A. Doan, Constraint-Based Entity Matching. Proceedings of the National Conference on Artificial Intelligence (AAAI) (2005)
- X. Li and D. Roth, Discriminative Training of Clustering Functions: Theory and Experiments with Entity Identification. Proc. of the Annual Conference on Computational Natural Language Learning (CoNLL) (2005) pp. 64--71
- X. Li, P. Morie, and D. Roth, Identification and Tracing of Ambiguous Names: Discriminative and Generative Approaches. Proceedings of the National Conference on Artificial Intelligence (AAAI) (2004) pp. 419--424
- X. Li, P. Morie, and D. Roth, Robust Reading: Identification and Tracing of Ambiguous Names. Proc. of the Annual Meeting of the North American Association of Computational Linguistics (NAACL) (2004) pp. 17--24
- X. Li, P. Morie, and D. Roth, Robust Reading of Ambiguous Writing. (2003)
