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        <title>Hashtag Recommendation with Topical Attention-Based LSTM</title>
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        <namePart type="given">Yang</namePart>
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        <namePart type="given">Liang</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4% improvement in F1 score compared with standard LSTM method.</abstract>
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%0 Conference Proceedings
%T Hashtag Recommendation with Topical Attention-Based LSTM
%A Li, Yang
%A Liu, Ting
%A Jiang, Jing
%A Zhang, Liang
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-hashtag
%X Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4% improvement in F1 score compared with standard LSTM method.
%U https://aclanthology.org/C16-1284
%P 3019-3029

Markdown (Informal)

[Hashtag Recommendation with Topical Attention-Based LSTM](https://aclanthology.org/C16-1284) (Li et al., COLING 2016)

• Hashtag Recommendation with Topical Attention-Based LSTM (Li et al., COLING 2016)

ACL

• Yang Li, Ting Liu, Jing Jiang, and Liang Zhang. 2016. Hashtag Recommendation with Topical Attention-Based LSTM. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3019–3029, Osaka, Japan. The COLING 2016 Organizing Committee.