A Hybrid Approach of Pattern Extraction and Semi-supervised Learning for Vietnamese Named Entity Recognition

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.