Multi-pattern fusion based semi-supervised Name Entity Recognition
Ziguang Cheng, Dequan Zheng, Sheng Li · 2013
Named Entity Recognition (NER) is one of the most important problems in Natural Language Processing (NLP). NER also has a broad prospect for application and important research value. There are a lot of methods and technology to solve NER problem. In this paper, for a specific application background, a new multi-pattern fusion based semi-supervised NER method is proposed. We use soft-matching method in entity internal pattern first. Then through bootstrapping process in the training corpus, we get an entity external pattern. Finally we use fusion internal and external pattern method to complete the named entity recognition. Experiments on Chinese weapon names, from People's Daily corpus and some military news articles were performed. They showed when the internal characteristic is significant and training corpus has a higher similarity with test corpus, this method performs better than soft matching method and external pattern based bootstrapping method, improving the named entity recognition precision by 18.2%.