自然语言处理下并行化命名实体识别OA

In the named entity recognition task in natural language processing,there is a significant difference in the length of text sequences,and word by word processing can lead to long computation time and low efficiency.Therefore,a parallel named entity recognition method in natural language processing is proposed.A parallel named entity recognition model is constructed by QRNN(quasi-recurrent neural network)and CRF(conditional random field)techniques in the field of natural language processing.Preprocessing operations such as denoising and text encoding are performed on the input text by a preprocessing layer,and the preprocessed text sequence is input to the QRNN layer.The QRNN layer constructs a network structure by using convolutional modules alternately,which can simultaneously process multiple positions in the text sequence and improve the efficiency,so as to extract in parallel the deep named entity contextual features in the text.The CRF layer decodes the named entity context feature vector output by the QRNN layer by considering the global information of the label sequence comprehensively,and the Viterbi algorithm is used to output the named entity label with the highest score,thus achieving parallel named entity recognition in the natural language processing.The experimental results show that the importance score of the contextual features extracted by the proposed method is over 90 points,which can provide key information support for named entity recognition and accurately identify all named entities in the text without any omissions.