Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction

Marco Basaldella, Giorgia Chiaradia, Carlo Tasso


Abstract
In this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact of these features, we define a baseline keyphrase extraction system and evaluate its performance on a standard dataset using different machine learning algorithms. Then, we consider new sets of features by adding combinations of the linguistic features we propose and we evaluate the new performance of the system. We also use anaphora and coreference resolution to transform the documents, trying to simulate the cohesion process performed by the human mind. We found that our approach has a slightly positive impact on the performance of automatic keyphrase extraction, in particular when considering the ranking of the results.
Anthology ID:
C16-1077
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
804–814
Language:
URL:
https://aclanthology.org/C16-1077
DOI:
Bibkey:
Cite (ACL):
Marco Basaldella, Giorgia Chiaradia, and Carlo Tasso. 2016. Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 804–814, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction (Basaldella et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1077.pdf