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Multi-Task CNN for Classification of Chinese Legal Questions
Guangyi Xiao1; Jiqian Mo1; Even Chow1; Hao Chen1; Jingzhi Guo2; Zhiguo Gong2
2017
Conference Name14th IEEE International Conference on e-Business Engineering (ICEBE)
Source Publication2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)
Pages84-90
Conference DateNOV 04-06, 2017
Conference PlaceShanghai, PEOPLES R CHINA
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
Abstract

This paper proposes a multi-task learning algorithm to classify the Chinese legal questions using deep convolutional neural networks (CNN). First, we propose a multi-task Convolutional Neural Network (CNN) for classification of Chinese legal questions with trainable word embedding where coarse grained classification is the main task and fine grained classification is the side task. Second, we develop a hierarchical classification model which takes the output of coarse classification as one part of the input for fine grained classification. We find that the side task can improve the accuracy and efficiency of the classification in a certain extent. Our experiments on the entire Chinese Legal Questions Dataset (LQDS) demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using almost all data in LQDS for classification and we achieve the state of the art performance.

KeywordQuestion Classification Cnn Multi-task Word2vec Hierarchical Classification
DOI10.1109/ICEBE.2017.22
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Operations Research & Management Science
WOS SubjectComputer Science, Interdisciplinary Applications ; Operations Research & Management Science
WOS IDWOS:000426981100012
The Source to ArticleWOS
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.College of computer science and electronic engineering Hunan University, Changsha, China
2.Faculty of Science and Technology, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Guangyi Xiao,Jiqian Mo,Even Chow,et al. Multi-Task CNN for Classification of Chinese Legal Questions[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:84-90.
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