文章基本信息

标题：A Machine Learning Approach to Coreference Resolution of Noun Phrases
本地全文：下载
作者：Wee Meng Soon ; Hwee Tou Ng ; Daniel Chung Yong Lim 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2001
卷号：27
期号：4
页码：521-544
DOI：10.1162/089120101753342653
语种：English
出版社：MIT Press
摘要：In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of “organization,” “person,” or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.