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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Zheng Li

Hong Kong University of Science and Technology


Ying Wei

Hong Kong University of Science and Technology


Yu Zhang

Hong Kong University of Science and Technology


Qiang Yang

Hong Kong University of Science and Technology


DOI:

10.1609/aaai.v32i1.12055


Abstract:

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classification in a target domain that has no or little supervised information. Existing cross-domain sentiment classification methods cannot automatically capture non-pivots, i.e., the domain-specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The proposed HATN provides a hierarchical attention transfer mechanism which can transfer attentions for emotions across domains by automatically capturing pivots and non-pivots. Besides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net aligning the non-pivots by using the pivots as a bridge. Specifically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P-net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.

Topics: AAAI

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HOW TO CITE:

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification AAAI 2018, .

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang (2018). Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang. Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang. 2018. Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang. (2018) "Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang, "Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification", AAAI, p., 2018.

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang. "Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang. "Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Zheng Li||Ying Wei||Yu Zhang||Qiang Yang. Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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