Anaphora resolution: to what extent does it help nlp applications?

Authors:
Ruslan Mitkov;Richard Evans;Constantin Orăsan;Le An Ha;Viktor Pekar
Affiliations:
University of Wolverhampton, Research Group in Computational Linguistics, Research Institute in Information and Language Processing, Wolverhampton, United Kingdom;University of Wolverhampton, Research Group in Computational Linguistics, Research Institute in Information and Language Processing, Wolverhampton, United Kingdom;University of Wolverhampton, Research Group in Computational Linguistics, Research Institute in Information and Language Processing, Wolverhampton, United Kingdom;University of Wolverhampton, Research Group in Computational Linguistics, Research Institute in Information and Language Processing, Wolverhampton, United Kingdom;University of Wolverhampton, Research Group in Computational Linguistics, Research Institute in Information and Language Processing, Wolverhampton, United Kingdom
Venue:
DAARC'07 Proceedings of the 6th discourse anaphora and anaphor resolution conference on Anaphora: analysis, algorithms and applications
Year:
2007

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Abstract

Papers discussing anaphora resolution algorithms or systems usually focus on the intrinsic evaluation of the algorithm/system and not on the issue of extrinsic evaluation. In the context of anaphora resolution, extrinsic evaluation concerns the impact of an anaphora resolution module on a larger NLP system of which it is part. In this paper we explore the extent to which the well-known anaphora resolution system MARS [1] can improve the performance of three NLP applications: text summarisation, term extraction and text categorisation. On the basis of the results so far we conclude that the deployment of anaphora resolution has a positive albeit limited impact.