关键词: 命名实体识别, 词向量, XLnet, 语言模型

Abstract:

The establishment of linguistic model has a direct impact on exploring the semantic information in sentences. To improve the recognition rate of Chinese named entities, the semantic representation of Chinese characters is the pointed. Aiming at the traditional Chinese named entity recognition algorithm has not fully tapped the hidden information inside the sentence, this article puts forward CAW-XLnet-BiGRU-CRF network framework by word-vector features generated by large-scale corpus pretraining with LSTM extract and uses CNN to extract spatial information between words, then integrates the extracted spatial information with the word vector features obtained and imports it into the language model XLnet （Generalized autoregressive pretraining for language understanding）, finally outputs the optimal tag sequence by BiGRU-CRF. The experiment result shows that the F1 value of the framework in the January 1998 data set of People’s Daily reachs 95.73% and solves the problem of hidden information inner, which can be well applied to Chinese named entity recognition task.

Key words: named entity recognition, word vector, XLnet, language model