Lao Named Entity Recognition based on Conditional Random Fields with Simple Heuristic Information

According to characteristics of Lao named entities, the paper proposes an approach of Lao Named Entity Recognition (NER) based on Conditional Random Fields (CRFs) with knowledge information. Firstly, we segment the text into word sequence and design three labels BIO1 for personal name and location name entity recognition. Secondly, some named entity features of Lao Language are selected for Conditional Random Fields (CRFs) model, such as the clue word feature, the predicate feature etc.. Then, candidate named entities are recognized. Thirdly, we extract simple personal name and location name features of Lao Language to build heuristic information, and use the heuristic information to determine candidate named entities. Finally, named entities which have not been discovered by Conditional Random Fields (CRFs) model are further recognized by using the named entities word list, and these final named entities are obtained. The experimental results show that the method proposed is effective, and it can improve the effect of named entity recognition by using machine learning method with heuristic information.