@inproceedings{ma-etal-2018-joint,
title = "Joint Learning for Targeted Sentiment Analysis",
author = "Ma, Dehong and
Li, Sujian and
Wang, Houfeng",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1504",
doi = "10.18653/v1/D18-1504",
pages = "4737--4742",
abstract = "Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes. Previous works only exploit word embeddings as features and do not explore more potentials of neural networks when jointly learning the two tasks. In this paper, we carefully design the hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model to learn abstract features for both tasks, and we propose a HSBi-GRU based joint model which allows the target label to have influence on their sentiment label. Experimental results on two datasets show that our joint learning model can outperform other baselines and demonstrate the effectiveness of HSBi-GRU in learning abstract features.",
}
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<abstract>Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes. Previous works only exploit word embeddings as features and do not explore more potentials of neural networks when jointly learning the two tasks. In this paper, we carefully design the hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model to learn abstract features for both tasks, and we propose a HSBi-GRU based joint model which allows the target label to have influence on their sentiment label. Experimental results on two datasets show that our joint learning model can outperform other baselines and demonstrate the effectiveness of HSBi-GRU in learning abstract features.</abstract>
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%0 Conference Proceedings
%T Joint Learning for Targeted Sentiment Analysis
%A Ma, Dehong
%A Li, Sujian
%A Wang, Houfeng
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ma-etal-2018-joint
%X Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes. Previous works only exploit word embeddings as features and do not explore more potentials of neural networks when jointly learning the two tasks. In this paper, we carefully design the hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model to learn abstract features for both tasks, and we propose a HSBi-GRU based joint model which allows the target label to have influence on their sentiment label. Experimental results on two datasets show that our joint learning model can outperform other baselines and demonstrate the effectiveness of HSBi-GRU in learning abstract features.
%R 10.18653/v1/D18-1504
%U https://aclanthology.org/D18-1504
%U https://doi.org/10.18653/v1/D18-1504
%P 4737-4742
Markdown (Informal)
[Joint Learning for Targeted Sentiment Analysis](https://aclanthology.org/D18-1504) (Ma et al., EMNLP 2018)
ACL
- Dehong Ma, Sujian Li, and Houfeng Wang. 2018. Joint Learning for Targeted Sentiment Analysis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4737–4742, Brussels, Belgium. Association for Computational Linguistics.