Supervised and semi-supervised sequence learning for recognition of requisite part and effectuation part in law sentences

Authors:
Le-Minh Nguyen;Ngo Xuan Bach;Akira Shimazu
Affiliations:
Japan Advanced Institute of Science and Technology (JAIST) Asahidai, Nomi, Ishikawa Japan;Japan Advanced Institute of Science and Technology (JAIST) Asahidai, Nomi, Ishikawa Japan;Japan Advanced Institute of Science and Technology (JAIST) Asahidai, Nomi, Ishikawa Japan
Venue:
FSMNLP '11 Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing
Year:
2011

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Abstract

Analyzing the logical structure of a sentence is important for understanding natural language. In this paper, we present a task of Recognition of Requisite Part and Effectuation Part in Law Sentences, or RRE task for short, which is studied in research on Legal Engineering. The goal of this task is to recognize the structure of a law sentence. We empirically investigate how the RRE task is conducted with respect to various supervised machine learning models. We also compared the impact of unlabeled data to RRE tasks. Experimental results for Japanese legal text domains showed that sequence learning models are suitable for RRE tasks and unlabled data also significantly contribute to the performance of RRE tasks.