Information Structure Parsing for Chinese Legal Texts

Abstract

Information processing is one of the main concerns in the field of artificial intelligence, because it can benefit many related downstream tasks. To facilitate information processing, information structure parsing is assumed to be of great significance. This article proposes a discourse analysis based approach so that information structure of Chinese legal texts can be recognized automatically. This article employs Discourse Information Theory to explore information features of Chinese legal texts. The texts used in this study include 6 types, each type containing 60 training texts and 30 testing texts. After that, a set of rules is formulated to classify legal texts and identify the categories of information units. Finally, to examine the performance of the rules, a comparison is made by designing a Support Vector Machine classifier and a Viterbi algorithm decoder. The experiment demonstrates that the rule based approach outperforms the statistics based approaches. This research suggests that discourse analysis may provide some linguistic features conducive to discourse parsing.