Sentiment Lexicon Enhanced Attention-based LSTM for Sentiment Classification
文献类型：会议论文
| 作者 | Zeyang Lei; Yujiu Yang; Min Yang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | New Orleans, Louisiana, USA |
| 英文摘要 | Deep neural networks have gained great success recently for sentiment classification. However, these approaches do not fully exploit the linguistic knowledge. In this paper, we propose a novel sentiment lexicon enhanced attention-based LSTM (SLEA-LSTM) model to improve the performance of sentence-level sentiment classification. Our method successfully integrates sentiment lexicon into deep neural networks via single-head or multi-head attention mechanisms. We conduct extensive experiments on MR and SST datasets. The experimental results show that our model achieved comparable or better performance than the state-of-the-art methods. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14094]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Zeyang Lei,Yujiu Yang,Min Yang. Sentiment Lexicon Enhanced Attention-based LSTM for Sentiment Classification[C]. 见:. New Orleans, Louisiana, USA. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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