Opinion Mining with Deep Contextualized Embeddings

Wen-Bin Han, Noriko Kando


Abstract
Detecting opinion expression is a potential and essential task in opinion mining that can be extended to advanced tasks. In this paper, we considered opinion expression detection as a sequence labeling task and exploited different deep contextualized embedders into the state-of-the-art architecture, composed of bidirectional long short-term memory (BiLSTM) and conditional random field (CRF). Our experimental results show that using different word embeddings can cause contrasting results, and the model can achieve remarkable scores with deep contextualized embeddings. Especially, using BERT embedder can significantly exceed using ELMo embedder.
Anthology ID:
N19-3006
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–42
Language:
URL:
https://aclanthology.org/N19-3006
DOI:
10.18653/v1/N19-3006
Bibkey:
Cite (ACL):
Wen-Bin Han and Noriko Kando. 2019. Opinion Mining with Deep Contextualized Embeddings. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 35–42, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Opinion Mining with Deep Contextualized Embeddings (Han & Kando, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-3006.pdf
Data
MPQA Opinion Corpus