A hybrid approach of pattern extraction and semi-supervised learning for vietnamese named entity recognition

Authors:
Duc-Thuan Vo;Cheol-Young Ock
Affiliations:
Natural Language Processing Lab, School of Computer Engineering and Information Technology, University of Ulsan, Korea;Natural Language Processing Lab, School of Computer Engineering and Information Technology, University of Ulsan, Korea
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Year:
2012

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Abstract

Requiring a large hand-annotated corpus in supervised learning of contemporary Vietnamese Named Entity Recognition researches is challenging. We therefore propose a hybrid approach of pattern extraction and semi-supervised learning. Applied rule-based method helps generating patterns automatically. Part-of-speech tagger, lexical diversity and chunking are explored to define rules in pattern extractions which are used for identifying potential named entities. Semi-supervised learning trains a small amount of seed named entities to categorize named entities in extracted patterns. In experiments, our approach shows good increasing the system accuracy with others in Vietnamese.