Keyphrase extraction for legal questions based on a sequence to sequence model

被引：0

作者：

Zeng D. ^{[1
,2
]}

Tong G. ^{[1
,2
]}

Dai Y. ^{[1
,2
]}

Li F. ^{[1
,2
]}

Han B. ^{[3
]}

Xie S. ^{[3
]}

机构：

[1] School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha

[2] Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha

[3] Hunan Date-driven AI Technology Co. Ltd., Changsha

来源：

Qinghua Daxue Xuebao/Journal of Tsinghua University | 2019年 / 59卷 / 04期

关键词：

Keyphrase extraction; Reinforcement learning; Sequence-to-sequence model;

D O I：

10.16511/j.cnki.qhdxxb.2019.21.007

中图分类号：

学科分类号：

摘要：

Traditional keyphrase extraction algorithms cannot extract keyphrases that have not appeared in the text, so they cannot effectively extract keyphrases in short legal texts. This paper presents a sequence-to-sequence (seq2seq) model based on reinforcement learning to extract keyphrases from legal questions. First, the encoder pushes the semantic information of a given legal question text into a dense vector; then, the decoder automatically generates the keyphrases. Since the order of the generated keyphrases does not matter in the keyphrase extraction task, reinforcement learning is used to train the model. This method combines the advantages of reinforcement learning for decision-making and the advantages of the sequence-to-sequence model for long-term memory. Tests on real datasets show that the model provides accurate keyphrase extraction. © 2019, Tsinghua University Press. All right reserved.

引用

页码：256 / 261

页数：5

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