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Data Analysis and Knowledge Discovery
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Keyword Extraction for Journals Based on Part-of-speech and BiLSTM-CRF Combined Model
Cheng Bin,Shi Shuicai,Du YunCheng,Xiao Shibin
(Computer School,Beijing Information Science and Technology University , Beijing 100185, China)
(Beijing TRS Information Technology Co., Ltd., Beijing 100101, China)
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[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 SW is improved by 3% compared to the original BiLSTM model. On CW, 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.

Key words keyword extraction      conditional random field      deep learning      Bidirectional Long Short Term Memory      
Published: 11 November 2020
ZTFLH:  TP393  

Cite this article:

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, 0, (): 1-.

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