Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

Hu Xu, Bing Liu, Lei Shu, Philip S. Yu


Abstract
One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.
Anthology ID:
P18-2094
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
592–598
Language:
URL:
https://www.aclweb.org/anthology/P18-2094
DOI:
10.18653/v1/P18-2094
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-2094.pdf
Poster:
 P18-2094.Poster.pdf