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2020 | OriginalPaper | Buchkapitel

Linguistic Knowledge Based on Attention Neural Network for Targeted Sentiment Classification

verfasst von : Chengyu Du, Pengyuan Liu

Erschienen in: Chinese Lexical Semantics

Verlag: Springer International Publishing

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Abstract

Deep learning approaches for targeted sentiment classification do not fully exploit linguistic knowledge. In this paper, we propose a Linguistic Knowledge based on Attention Neural Network (LKAN) to employ linguistic knowledge (e.g. sentiment lexicon, negation words, intensity words) to benefit targeted sentiment classification. Firstly, we extract linguistic knowledge words (e.g. sentiment lexicon, negation words, intensity words) in sentences by HowNet vocabulary. Then, we design an attention mechanism which drives the model to concentrate on such words and get a weighted combination of word embeddings as the final representation for the sentences. We evaluate our proposed approach on SemEval 2014 Task 4, whose performance as shown reaches the most advanced level.

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Vorheriges Kapitel An Easier and Efficient Framework to Annotate Semantic Roles: Evidence from the Chinese AMR Corpus

Nächstes Kapitel A Method of Automatic Memorabilia Generation Based on News Reports

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Titel: Linguistic Knowledge Based on Attention Neural Network for Targeted Sentiment Classification
verfasst von: Chengyu Du
Pengyuan Liu
Verlag: Springer International Publishing
Buch: Chinese Lexical Semantics
Print ISBN: 978-3-030-38188-2

Electronic ISBN: 978-3-030-38189-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-38189-9_50

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