Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

	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
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.