Named entity recognition based on bilingual co-training

Named entity recognition (NER) is a very important task in natural language processing (NLP). In this paper we present a semi-supervised approach to extract bilingual named entity, starting from a bilingual corpus where the named entities are extracted independently for each language. Then a bilingual co-training algorithm is used to improve the named entity annotation quality, and iterative process is applied to extract named entity pairs with higher bilingual conformity ratio. This leads to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the annotation quality of Chinese NE is improved from 87.17 to 88.28, and improved 80.37 to 81.76 of English NE in F-measure.