@inproceedings{3ac813a4c2ef45a78e49e97a52469310,
title = "Relation Extraction Based on Dual Attention Mechanism",
abstract = "The traditional deep learning model has problems that the long-distance dependent information cannot be learned, and the correlation between the input and output of the model is not considered. And the information processing on the sentence set is still insufficient. Aiming at the above problems, a relation extraction method combining bidirectional GRU network and multi-attention mechanism is proposed. The word-level attention mechanism was used to extract the word-level features from the sentence, and the sentence-level attention mechanism was used to focus on the characteristics of sentence sets. The experimental verification in the NYT dataset was conducted. The experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.",
keywords = "Bidirectional GRU, Multi-attention, Relation extraction",
author = "Xue Li and Yuan Rao and Long Sun and Yi Lu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019 ; Conference date: 20-09-2019 Through 23-09-2019",
year = "2019",
doi = "10.1007/978-981-15-0118-0\_27",
language = "英语",
isbn = "9789811501173",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "346--356",
editor = "Xiaohui Cheng and Weipeng Jing and Xianhua Song and Zeguang Lu",
booktitle = "Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings",
}