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Erschienen in:
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Improving Short Text Modeling by Two-Level Attention Networks for Sentiment Classification

verfasst von : Yulong Li, Yi Cai, Ho-fung Leung, Qing Li

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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Abstract

Understanding short texts is crucial to many applications, but it has always been challenging, due to the sparsity and ambiguity of information in short texts. In addition, sentiments expressed in those user-generated short texts are often implicit and context dependent. To address this, we propose a novel model based on two-level attention networks to identify the sentiment of short text. Our model first adopts attention mechanism to capture both local features and long-distance dependent features simultaneously, so that it is more robust against irrelevant information. Then the attention-based features are non-linearly combined with a bidirectional recurrent attention network, which enhances the expressive power of our model and automatically captures more relevant feature combinations. We evaluate the performance of our model on MR, SST-1 and SST-2 datasets. The experimental results show that our model can outperform the previous methods.

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Vorheriges Kapitel Interpreting Fine-Grained Categories from Natural Language Queries of Entity Search
Nächstes Kapitel Efficient and Scalable Mining of Frequent Subgraphs Using Distributed Graph Processing Systems
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Metadaten
Titel
Improving Short Text Modeling by Two-Level Attention Networks for Sentiment Classification
verfasst von
Yulong Li
Yi Cai
Ho-fung Leung
Qing Li
Copyright-Jahr
2018
Buch
Database Systems for Advanced Applications

Print ISBN: 978-3-319-91451-0

Electronic ISBN: 978-3-319-91452-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-91452-7_56