Fuzziness Detection in Thai Law Texts Using Deep Learning

Abstract

Machine understanding research aims to build machine intelligences. To make a machine understand, precise concepts are necessary. Numerous domains contain vague meanings when making decisions, such as a diagnosis or a legal interpretation. Once an artificial intelligence pretends to be human while dealing with imprecise data, a fuzziness in knowledges must be detected before constructing.This paper presents the methodology to detect a fuzziness in Thai law texts using a deep learning method. The experiments are designed to compare the performances of four well-known text classification methods, namely Decision Tree, Random Forest, Support Vector Machine, and Convolutional Neural Network. The fuzziness in this study refers to an imprecise meaning in law texts which may be ambiguous when interpreted by a machine. We built a labelled corpus from four Thai Law codes namely 1) The Criminal Code 2) The Criminal Procedure Code 3) The Civil and Commercial Code and 4) The Civil Procedure Code. We proposed three conditions to identify the fuzziness, i.e. 1) a decision depends on a judge’s opinion 2) a decision that requires the production of evidence and 3) a decision which refers to other sections. The results of the experiment show that a Convolutional Neural Network significantly outperforms the others with 97.54% accuracy in comparison of all the dataset.