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Return to Session VIIa: Recognising and using named entities
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy.
@inproceedings{1009042, author = {Li Zhang and Yue Pan and Tong Zhang}, title = {Focused named entity recognition using machine learning}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {281--288}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009042}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
