[Objective] Utilizing the advantages of the CRF model to solve the problem of sequence labeling, by incorporating part-of-speech information and the CRF model into the BiLSTM network, automatic extraction of journal keywords is realized. [Methods] The keyword extraction problem is considered as a sequence labeling problem. Pre-processing word segmentation and part-of-speech tagging of journal text; vectorizing the pre-processed text using the Word2Vec model for Word Embedding to obtain vector expressions of words; using BiLSTM-CRF model for automatic keyword extraction. [Results] Using the part-of-speech and BiLSTM-CRF network to perform experiments on the collected China National Knowledge Infrastructure text, the accuracy on Simple Word is improved by 3% compared to the original BiLSTM model. On Complex Word, the accuracy is improved by 12%. [Limitations] The journal keyword extraction model cannot accurately extract complex keywords. In future work, it is necessary to further remind the model of the performance of complex keywords. [Conclusions] Compared with the traditional method, the BiLSTM-CRF model with part-of-speech integration has higher recognition accuracy and is an effective keyword extraction method.
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Cheng Bin,Shi Shuicai,Du Yuncheng,Xiao Shibin. Keyword Extraction for Journals Based on Part-of-Speech and BiLSTM-CRF Combined Model. Data Analysis and Knowledge Discovery, 2021, 5(3): 101-108.
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