Word-Character Model with Low Lexical Information Loss for Chinese NER
Chinese named entity recognition(CNER)task is a natural language processing technique that aims to recognize enti-ties with specific categories in text,such as names of people,places,organizations.It is a fundamental underlying task of natural language applications such as question and answer systems,machine translation,and information extraction.Since Chinese does not have a natural word separation structure like English,the effectiveness of word-based NER models for Chinese named entity recognition is significantly reduced by word separation errors,and character-based NER models ignore the role of lexical informa-tion.In recent years,many studies have attempted to incorporate lexical information into character-based models,and WC-LSTM has achieved significant improvements in model performance by injecting lexical information into the start and end characters of a word.However,this model still does not fully utilize lexical information,so based on it,LLL-WCM(word-character model with low lexical information loss)is proposed to incorporate lexical information for all intermediate characters of the lexicon to avoid lexical information loss.Meanwhile,two encoding strategies average and self-attention mechanism are introduced to extract all lexical information.Experiments are conducted on four Chinese datasets,and the results show that the F1 values of this method are improved by 1.89％,0.29％,1.10％and 1.54％,respectively,compared with WC-LSTM.
Named entity recognitionNatural language processingLexical information lossIntermediate charactersEncoding strategy
万方数据
维普
