Latent Variable Sentiment Grammar

Liwen Zhang, Kewei Tu, Yue Zhang


Abstract
Neural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to encode sentiment class labels. To this end, we investigate two formalisms with deep sentiment representations that capture sentiment subtype expressions by latent variables and Gaussian mixture vectors, respectively. Experiments on Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar over vanilla neural encoders. Using ELMo embeddings, our method gives the best results on this benchmark.
Anthology ID:
P19-1457
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4642–4651
Language:
URL:
https://aclanthology.org/P19-1457
DOI:
10.18653/v1/P19-1457
Bibkey:
Cite (ACL):
Liwen Zhang, Kewei Tu, and Yue Zhang. 2019. Latent Variable Sentiment Grammar. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4642–4651, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Latent Variable Sentiment Grammar (Zhang et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1457.pdf
Code
 Ehaschia/bi-tree-lstm-crf
Data
SSTSST-2SST-5