An Approach of Rhetorical Status Recognition for Judgments in Court Documents using Deep Learning Models
An Approach of Rhetorical Status Recognition for Judgments in Court Documents using Deep Learning Models
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DOI：
10.1109/kse.2019.8919370
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Vu Tran;M. Nguyen;Kiyoaki Shirai;K. Satoh
中科院分区：
文献类型：
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作者：
Vu Tran;M. Nguyen;Kiyoaki Shirai;K. Satoh
在法庭文书中，句子的修辞状态传达了句子的意图，无论是主张还是包含支持证据，因此有利于法院文书处理系统，例如法院文书检索系统。此外，修辞结构分析在自然语言处理中有着重要的应用，如文本摘要、情感分析、问答等。分析的输出结构包含了小句之间的高层关系，因此提供了有价值的信息。尽管自动修辞结构分析在法律领域有着广泛的应用和自动处理的必要性，但在法律领域，自动修辞结构分析并没有得到很好的重视。我们建议使用深度学习模型来处理识别法庭文件中每一句话的修辞状态的任务。深度学习已被证明对自然语言处理任务包括语篇分析是有效的。本课题取得了良好的实验结果，表明人工神经模块嵌入修辞信息可以应用于摘要、信息检索等其他任务。
In a court document, the rhetorical status of a sentence conveys the intention of the sentence, whether is is a claim or contains supporting evidences, thus, is beneficial to court document processing systems, for example, court document retrieval systems. Besides, rhetorical structure analysis has high-impact applications in natural language processing, for instances, text summarization, sentiment analysis, question answering. The output structures of the analysis contain high-level relationship between clauses and so provides valuable information. Despite of a wide range of applications and the necessity for automatic court document processing, automatic rhetorical structure analysis has not been well noticed in the legal domain. We propose to use deep learning models for tackling the task of recognizing the rhetorical status of each sentence in a court document. Deep learning has been shown effective towards natural language processing tasks including discourse analysis. We have achieved promising results for the task, which suggests the applicability of artificial neural module embedding rhetorical information for other tasks, for example, summarization and information retrieval.