Recognizing logical parts in Vietnamese legal texts using Conditional Random Fields

• Published in: The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF) pp. 1 - 6
• Main Authors: Son, Nguyen Truong, Phuong Duyen, Nguyen Thi, Quoc, Ho Bao, Minh, Nguyen Le
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2015
• Subjects: conditional random field; Dictionaries; Hidden Markov models; Law; legal text mining; machine learning; named entities recognition; Ontologies; semantic annotation; Text recognition; Training
• ISBN: 9781479980437; 1479980439
• DOI: 10.1109/RIVF.2015.7049865

Analyzing the structure of legal sentences in legal document is an important phase to build a knowledge management system in Legal Engineering. This paper proposes a new approach to recognize logical parts in Vietnamese legal documents based on a statistic machine learning method - Conditional Random Fields. Beside linguistic features such as word features, part of speech features, we use semantic features of logical parts such as trigger features and ontology features to improve the result of the annotation system. Experiments were conducted in a Vietnamese Business Law data set and obtained 78.12% at precision and 68.72% at recall measure. Compare to state-of-the-art systems, it improves the result for recognizing some logical parts.