@inproceedings{ghosal-etal-2020-kingdom,
title = "{K}in{GDOM}: {K}nowledge-{G}uided {DOM}ain {A}daptation for {S}entiment {A}nalysis",
author = "Ghosal, Deepanway and
Hazarika, Devamanyu and
Roy, Abhinaba and
Majumder, Navonil and
Mihalcea, Rada and
Poria, Soujanya",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.292",
doi = "10.18653/v1/2020.acl-main.292",
pages = "3198--3210",
abstract = "Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.",
}
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<abstract>Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.</abstract>
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%0 Conference Proceedings
%T KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis
%A Ghosal, Deepanway
%A Hazarika, Devamanyu
%A Roy, Abhinaba
%A Majumder, Navonil
%A Mihalcea, Rada
%A Poria, Soujanya
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ghosal-etal-2020-kingdom
%X Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.
%R 10.18653/v1/2020.acl-main.292
%U https://aclanthology.org/2020.acl-main.292
%U https://doi.org/10.18653/v1/2020.acl-main.292
%P 3198-3210
Markdown (Informal)
[KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis](https://aclanthology.org/2020.acl-main.292) (Ghosal et al., ACL 2020)
ACL
- Deepanway Ghosal, Devamanyu Hazarika, Abhinaba Roy, Navonil Majumder, Rada Mihalcea, and Soujanya Poria. 2020. KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3198–3210, Online. Association for Computational Linguistics.