A Machine Learning Approach to Argument Mining in Legal Documents

Autor:	Prakash Poudyal
Rok vydání:	2018
Předmět:	Argumentative Computer science business.industry Machine learning computer.software_genre Random forest Identifier Set (abstract data type) Argument Artificial intelligence Element (category theory) Cluster analysis business computer Natural language
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030001773 AICOL
DOI:	10.1007/978-3-030-00178-0_30
Popis:	This study aims to analyze and evaluate the natural language arguments present in legal documents. The research is divided into three modules or stages: an Argument Element Identifier Module identifying argumentative and non-argumentative sentences in legal texts; an Argument Builder Module handling clustering of argument’s components; and an Argument Structurer Module distinguishing argument’s components (premises and conclusion). The corpus selected for this research was the set of Case-Laws issued by the European Court of Human Rights (ECHR) annotated by Mochales-Palau and Moens [8]. The preliminary results of the Argument Element Identifier Module are presented, including its main features. The performance of two machine learning algorithms (Support Vector Machine Algorithm and Random Forest Algorithm) is also measured.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ddbdeee3a1eb559941522b6732517162 https://doi.org/10.1007/978-3-030-00178-0_30 Zobrazit plný text záznamu