Structural Attention Neural Networks for improved sentiment analysis

Filippos Kokkinos, Alexandros Potamianos

Abstract
We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree.
Anthology ID:
E17-2093
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
586–591
Language:
URL:
https://aclanthology.org/E17-2093
DOI:
Bibkey:
Cite (ACL):
Filippos Kokkinos and Alexandros Potamianos. 2017. Structural Attention Neural Networks for improved sentiment analysis. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 586–591, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Structural Attention Neural Networks for improved sentiment analysis (Kokkinos & Potamianos, EACL 2017)
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