A Novel Method for Chinese Named Entity Recognition Based on Character Vector

In this paper, a novel method using for Chinese named entity recognition is proposed. For each class, A posteriori probability model is acquired by combing probabilistic model and character vector, which are acquired from each class by using training data. After segment Chinese sentence into words, the posteriori probability of every words in each class can be calculated by using model we proposed, and thus the type of word could be determined according to maximum posteriori probability.