Commercial intelligence entity recognition model based on BiLSTM-CRF
摘要
A BiLSTM-CRF model is constructed by combining the Conditional Random Field(CRF) model of Bidirectional Long Short-Term Memory(BiLSTM) network to extract three kinds of entity information,tenderer,bidding agent and bidding number,in a commercial text sequence.The normalized bidding text sequence is vectorized by word.The forward and backward text features of the serialized text are obtained by BiLSTM neural network,and the corresponding entities in the two-way text features are extracted by CRF.Experimental results show that compared with the traditional machine learning algorithm CRF,the precision,recall rate and F1 value of the three types of entities in the proposed model are improved by 15.21%,12.06% and 13.70% in average,respectively.
引用本文（GB/T 7714）
ZHANG Yingcheng,YANG Yang,JIANG Rui,QUAN Bing,ZHANG Lijun,REN Xiaolei. Commercial intelligence entity recognition model based on BiLSTM-CRF[J]. DOAJ (DOAJ: Directory of Open Access Journals), 2019.
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