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Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text
Expert Systems ( IF 3.3 ) Pub Date : 2021-01-05 , DOI: 10.1111/exsy.12656
Anu Thomas 1 , Sivanesan Sangeetha 1
Affiliation  

Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article, we propose a knowledge‐driven, semi‐supervised pattern‐based learning (bootstrapping) approach to extract domain‐specific facts from judicial text, starting with a small set of seed facts. In the semantic analysis of legal text, fact extraction is the next step to entity identification, which involves the identification of roles played by each entity in the judicial text. The proposed methodology learns extraction patterns for 12 classes of facts from the judicial text through the integration of the domain ontology called judicial case ontology (JCO). The experimental results were evaluated by human judges and found to be quite promising. One main feature of the proposed methodology is its portability across various domains (such as medical, banking, insurance, etc.) which in turn helps build expert systems in various sectors.

中文翻译:

半监督,知识整合的模式学习方法,可从司法文本中提取事实

司法文件可用性的巨大增长要求信息提取(IE)技术的兴起,这些技术支持从司法文本中自动提取相关概念或数据。在可用于IE的各种方法中,基于本体的IE已被证明是最适合从自然语言文本中提取领域特定信息的方法。通过本文,我们提出了一种知识驱动的,半监督的基于模式的学习(引导)方法,从一小部分种子事实开始,从司法文本中提取特定领域的事实。在法律文本的语义分析中,事实提取是实体标识的下一步,这涉及到标识每个实体在司法文本中所扮演的角色。所提出的方法论通过整合称为司法案件本体(JCO)的领域本体,从司法文本中学习了12类事实的提取模式。实验结果由人类法官进行了评估,并被证明是很有前途的。所提出的方法的一个主要特征是其在各个领域(例如医疗,银行,保险等)的可移植性,这反过来又有助于在各个领域建立专家系统。
更新日期:2021-01-05
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