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

被引：1334

作者：

Zhou, Peng ^{[1
]}

Shi, Wei ^{[1
]}

Tian, Jun ^{[1
]}

Qi, Zhenyu ^{[1
]}

Li, Bingchen ^{[1
]}

Hao, Hongwei ^{[1
]}

Xu, Bo ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2 | 2016年

基金：

国家高技术研究发展计划(863计划);

关键词：

D O I：

10.18653/v1/p16-2034

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：207 / 212

页数：6

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