Aspect Extraction Using Coreference Resolution and Unsupervised Filtering

Deon Mai, Wei Emma Zhang

Abstract
Aspect extraction is a widely researched field of natural language processing in which aspects are identified from the text as a means for information. For example, in aspect-based sentiment analysis (ABSA), aspects need to be first identified. Previous studies have introduced various approaches to increasing accuracy, although leaving room for further improvement. In a practical situation where the examined dataset is lacking labels, to fine-tune the process a novel unsupervised approach is proposed, combining a lexical rule-based approach with coreference resolution. The model increases accuracy through the recognition and removal of coreferring aspects. Experimental evaluations are performed on two benchmark datasets, demonstrating the greater performance of our approach to extracting coherent aspects through outperforming the baseline approaches.
Anthology ID:
2020.aacl-srw.18
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Boaz Shmueli, Yin Jou Huang
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
124–129
Language:
URL:
https://aclanthology.org/2020.aacl-srw.18
DOI:
Bibkey:
Cite (ACL):
Deon Mai and Wei Emma Zhang. 2020. Aspect Extraction Using Coreference Resolution and Unsupervised Filtering. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 124–129, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (Mai & Zhang, AACL 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.aacl-srw.18.pdf
Data
SemEval-2014 Task-4
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