UM
Relation classification using coarse and fine-grained networks with SDP supervised key words selection
Sun, Yiping1,2; Cui, Yu1,2; Hu, Jinglu2; Jia, Weijia1,3
2018
Conference Name11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11061 LNAI
Pages514-522
Conference Date8 17, 2018 - 8 19, 2018
Conference PlaceChangchun, China
Author of SourceSpringer Verlag
AbstractIn relation classification, previous work focused on either whole sentence or key words, meeting problems when sentence contains noise or key words are extracted falsely. In this paper, we propose coarse and fine-grained networks for relation classification, which combine sentence and key words together to be more robust. Then, we propose a word selection network under shortest dependency path (SDP) supervision to select key words automatically instead of pre-processed key words and attention, which guides word selection network to a better feature space. A novel opposite loss is also proposed by pushing useful information in unselected words back to selected ones. In SemEval-2010 Task 8, results show that under the same features, proposed method outperforms state-of-the-art methods for relation classification. © 2018, Springer Nature Switzerland AG.
DOI10.1007/978-3-319-99365-2_46
Language英语
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China;
2.Graduate School of Information, Production and Systems, Waseda University, Kitakyushu-shi, Japan;
3.University of Macau, Macau, China
Recommended Citation
GB/T 7714
Sun, Yiping,Cui, Yu,Hu, Jinglu,et al. Relation classification using coarse and fine-grained networks with SDP supervised key words selection[C]//Springer Verlag,2018:514-522.
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