CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
Peng Zhou; Wei Shi; Jun Tian; Zhenyu Qi; Bingchen Li; Hongwei Hao; Bo Xu
2016
Conference NameAnnual meeting of the Association for Computational Linguistics
Pages207-212
Conference Date2016/8/7-2016/8/12
Conference PlaceBerlin, Germany
AbstractRelation 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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20945
Collection数字内容技术与服务研究中心_听觉模型与认知计算
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Peng Zhou,Wei Shi,Jun Tian,et al. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification[C],2016:207-212.
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