JOURNAL ARTICLE

Using mention accessibility to improve coreference resolution

Abstract

Modern coreference resolution systems require linguistic and general knowledge typically sourced from costly, manually curated resources.Despite their intuitive appeal, results have been mixed.In this work, we instead implement fine-grained surface-level features motivated by cognitive theory.Our novel fine-grained feature specialisation approach significantly improves the performance of a strong baseline, achieving state-of-the-art results of 65.29 and 61.13% on CoNLL-2012 using gold and automatic preprocessing, with system extracted mentions.

Keywords:
Coreference Computer science Preprocessor Baseline (sea) Resolution (logic) Feature (linguistics) Natural language processing Artificial intelligence Linguistics

Metrics

4
Cited By
0.85
FWCI (Field Weighted Citation Impact)
21
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and dialogue systems
Physical Sciences →  Computer Science →  Artificial Intelligence

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