Hashtag recommendation with topical attention-based LSTM
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 oflong short-term m...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/3436 https://ink.library.smu.edu.sg/context/sis_research/article/4437/viewcontent/C16_1284__1_.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 oflong short-term memory (LSTM) for many natural language processing tasks, in this paper, weadopt LSTM to learn the representation of a microblog post. Observing that hashtags indicatethe primary topics of microblog posts, we propose a novel attention-based LSTM model whichincorporates topic modeling into the LSTM architecture through an attention mechanism. Weevaluate our model using a large real-world dataset. Experimental results show that our modelsignificantly outperforms various competitive baseline methods. Furthermore, the incorporationof topical attention mechanism gives more than 7.4% improvement in F1 score compared withstandard LSTM method. |
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