Abstract: Traditional named entity recognition methods directly rely on plenty of hand-crafted features and special domain knowledge,and have resolved the problem that there are few supervised learning corpora which are available.But the costs of developing hand-crafted features and obtaining domain knowledge are expensive.To solve this problem,a neural network model based on BLSTM(Bidirectional Long Short-Term Memory) was proposed.This method does not directly use hand-crafted features and domain knowledge any more,but utilizes the word embedding based on context and word embedding based on characters.The former expresses the information about context of named entities,and the latter expresses the information about prefix,postfix and domain knowledge which make up the named entities.Simultaneously,it constrains the cost function of BLSTM by using the dependency between the labels in tagged sequence,and integrates the domain knowledge into the cost function,furtherly improving the recognition ability of the model.The experiments show that the recognition effect of the method in this paper is superior to traditional methods.

Key words: BLSTM,Named entity,Word embedding,Cost function