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2019 | OriginalPaper | Buchkapitel

Targeted Sentiment Classification with Attentional Encoder Network

verfasst von : Youwei Song, Jiahai Wang, Tao Jiang, Zhiyue Liu, Yanghui Rao

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series

Verlag: Springer International Publishing

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Abstract

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.

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Vorheriges Kapitel Collaborative Attention Network with Word and N-Gram Sequences Modeling for Sentiment Classification

Nächstes Kapitel Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Attention and Neural Collaborative Filtering

The detailed introduction of this task can be found at http://alt.qcri.org/semeval2014/task4.

We use uncased BERT-base from https://github.com/google-research/bert.

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Titel: Targeted Sentiment Classification with Attentional Encoder Network
verfasst von: Youwei Song
Jiahai Wang
Tao Jiang
Zhiyue Liu
Yanghui Rao
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series
Print ISBN: 978-3-030-30489-8

Electronic ISBN: 978-3-030-30490-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-30490-4_9

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