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A Machine Learning Approach to Coreference Resolution of Noun Phrases (2001)
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@MISC{Soon01amachine,
author = {Wee Meng Soon and Daniel Chung and Daniel Chung Yong Lim and Yong Lim and Hwee Tou Ng},
title = {A Machine Learning Approach to Coreference Resolution of Noun Phrases},
year = {2001}
}
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Abstract
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
Keyphrases
noun phrase machine learning approach coreference resolution annotated corpus certain type unrestricted text encouraging result general noun phrase coreference task common data set data set entity type learning approach performance comparable general noun phrase muc-7 coreference corpus first learning-based system
