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.
By: Li Zhang, Yue Pan, Tong Zhang
Published in: Proceedings of Sheffield SIGIR 2004: The 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, , ACM., p.281-8 in 2004
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