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Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter - 2016

Research Area:  Machine Learning

Abstract:

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we considered the problem of automatically extracting keyphrases from tweets. Because of the length limitations of Twitter-like sites, the performances of existing methods usually drop sharply. We proposed a novel deep recurrent neural network (RNN) model to combine keywords and context information to perform this problem. To evaluate the proposed method, we also constructed a large-scale dataset collected from Twitter. The experimental results showed that the proposed method performs significantly better than previous methods.

Author(s) Name:  Qi Zhang, Yang Wang, Yeyun Gong, Xuanjing Huang

Journal name:  

Conferrence name:  Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Publisher name:  Association for Computational Linguistics

DOI:  10.18653/v1/D16-1080

Volume Information: