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        <title>Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts</title>
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        <namePart type="given">Chengzhi</namePart>
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    <abstract>Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.</abstract>
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%0 Conference Proceedings
%T Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts
%A Zhang, Yingyi
%A Li, Jing
%A Song, Yan
%A Zhang, Chengzhi
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F zhang-etal-2018-encoding
%X Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.
%R 10.18653/v1/N18-1151
%U https://aclanthology.org/N18-1151
%U https://doi.org/10.18653/v1/N18-1151
%P 1676-1686

Markdown (Informal)

[Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts](https://aclanthology.org/N18-1151) (Zhang et al., NAACL 2018)

• Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (Zhang et al., NAACL 2018)

ACL

• Yingyi Zhang, Jing Li, Yan Song, and Chengzhi Zhang. 2018. Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1676–1686, New Orleans, Louisiana. Association for Computational Linguistics.