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Hierarchical Attention BLSTM for Modeling Sentences and Documents

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Recently, neural network based methods have made remarkable progresses on various Natural Language Processing (NLP) tasks. However, it is still a challenge to model both short and long texts, e.g. sentences and documents. In this paper, we propose a Hierarchical Attention Bidirectional LSTM (HA-BLSTM) to model both sentences and documents. HA-BLSTM effectively obtains a hierarchy of representations from words to phrases through the hierarchical structure. We design two attention mechanisms: local and global attention mechanisms. The local attention mechanism learns which components of a text are more important for modeling the whole text, while the global attention mechanism learns which representations of the same text are crucial. Thus, HA-BLSTM can model long documents along with short sentences. Experiments on four benchmark datasets show that our model yields a superior classification performance over a number of strong baselines.

Keywords

BLSTM Attention Text modeling 

Notes

Acknowledgments

This work is funded in part by the Chinese 863 Program (grant No. 2015AA015403), the Key Project of Tianjin Natural Science Foundation (grant No. 15JCZDJC31100), the Tianjin Younger Natural Science Foundation (Grant no: 14JCQNJC00400), the Major Project of Chinese National Social Science Fund (grant No. 14ZDB153) and MSCA-ITN-ETN - European Training Networks Project (grant No. 721321, QUARTZ).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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