Negative Focus Detection via Contextual Attention Mechanism

Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, AiTi Aw


Abstract
Negation is a universal but complicated linguistic phenomenon, which has received considerable attention from the NLP community over the last decade, since a negated statement often carries both an explicit negative focus and implicit positive meanings. For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context. However, how to capture contextual information for negative focus detection is still an open challenge. To well address this, we come up with an attention-based neural network to model contextual information. In particular, we introduce a framework which consists of a Bidirectional Long Short-Term Memory (BiLSTM) neural network and a Conditional Random Fields (CRF) layer to effectively encode the order information and the long-range context dependency in a sentence. Moreover, we design two types of attention mechanisms, word-level contextual attention and topic-level contextual attention, to take advantage of contextual information across sentences from both the word perspective and the topic perspective, respectively. Experimental results on the SEM’12 shared task corpus show that our approach achieves the best performance on negative focus detection, yielding an absolute improvement of 2.11% over the state-of-the-art. This demonstrates the great effectiveness of the two types of contextual attention mechanisms.
Anthology ID:
D19-1230
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2251–2261
Language:
URL:
https://aclanthology.org/D19-1230
DOI:
10.18653/v1/D19-1230
Bibkey:
Cite (ACL):
Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, and AiTi Aw. 2019. Negative Focus Detection via Contextual Attention Mechanism. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2251–2261, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Negative Focus Detection via Contextual Attention Mechanism (Shen et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1230.pdf