Chinese Named Entity Recognition Based on Bert Fusion Vocabulary
Named entity recognition is a very important task in natural language processing.It is crucial to correctly understand the entities in a sentence,and Chinese named entity recognition is more difficult than English because Chinese does not have boundary markers like spaces in English,and there is a complex nesting phenomenon.In response to the problem that most existing Chinese named entity recognition methods only use single level features,a fusion model of Bert Chinese pre training set and additional vocabulary dataset is used to enhance word meaning and Chinese context connection.BiGRU network is used to obtain sequence feature matrix,and a conditional random field model is used to generate the global optimal sequence,thereby improving the accuracy of entity recognition.The experimental results show that this method outperforms existing models on public datasets.
natural language processingnamed entity recognitionword combinationdeep learning
NETL
NSTL
万方数据
