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https://doi.org/10.3115/107308...
Article . 2001 . Peer-reviewed
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Improving machine learning approaches to coreference resolution

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Jan 2001Publisher:Association for Computational Linguistics (ACL)Journal:Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02Funded by:NSF | POWRE-Integrating Natural..., NSF | ITR: The Construction and...
Authors: Vincent Ng 0001; Claire Gardent;

doi: 10.3115/1073083.1073102

Improving machine learning approaches to coreference resolution

Abstract

We present a noun phrase coreference system that extends the work of Soon et al. (2001) and, to our knowledge, produces the best results to date on the MUC-6 and MUC-7 coreference resolution data sets --- F-measures of 70.4 and 63.4, respectively. Improvements arise from two sources: extra-linguistic changes to the learning framework and a large-scale expansion of the feature set to include more sophisticated linguistic knowledge.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
161
Top 10%
Top 1%
Average
bronze