Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification | |
Peng Zhou![]() ![]() ![]() ![]() ![]() ![]() | |
2016 | |
会议日期 | 2016/8/7-2016/8/12 |
会议地点 | Berlin, Germany |
页码 | 207-212 |
英文摘要 | Relation classification is an important semantic processing task in the field of natural language processing (NLP). State-of-the-art systems still rely on lexical resources such as WordNet or NLP systems like dependency parser and named entity recognizers (NER) to get high-level features. Another challenge is that important information can appear at any position in the sentence. To tackle these problems, we propose Attention-Based Bidirectional Long Short-Term Memory Networks(Att-BLSTM) to capture the most important semantic information in a sentence. The experimental results on the SemEval-2010 relation classification task show that our method outperforms most of the existing methods, with only word vectors. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/20945] ![]() |
专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
作者单位 | Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Peng Zhou,Wei Shi,Jun Tian,et al. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification[C]. 见:. Berlin, Germany. 2016/8/7-2016/8/12. |
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