@inproceedings{hazarika-etal-2018-modeling,
title = "Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis",
author = "Hazarika, Devamanyu and
Poria, Soujanya and
Vij, Prateek and
Krishnamurthy, Gangeshwar and
Cambria, Erik and
Zimmermann, Roger",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2043",
doi = "10.18653/v1/N18-2043",
pages = "266--270",
abstract = "Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.",
}
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<abstract>Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.</abstract>
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%0 Conference Proceedings
%T Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
%A Hazarika, Devamanyu
%A Poria, Soujanya
%A Vij, Prateek
%A Krishnamurthy, Gangeshwar
%A Cambria, Erik
%A Zimmermann, Roger
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F hazarika-etal-2018-modeling
%X Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.
%R 10.18653/v1/N18-2043
%U https://aclanthology.org/N18-2043
%U https://doi.org/10.18653/v1/N18-2043
%P 266-270
Markdown (Informal)
[Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis](https://aclanthology.org/N18-2043) (Hazarika et al., NAACL 2018)
ACL
- Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, and Roger Zimmermann. 2018. Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 266–270, New Orleans, Louisiana. Association for Computational Linguistics.