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| Building Phrase Dictionary for Defective Products with Convolutional Neural Network |
Peng Chen1,Lv Xueqiang1,Sun Ning2( ),Zang Le1,Jiang Zhaocai2,Song Li2 |
1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China
2China National Institute of Standardization, Beijing 100191, China |
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Abstract [Objective] This paper builds a dictionary for defective products, aiming to helps users better understand the latest developments of specific domains. [Methods] First, we extracted domain-related phrases from the corpus using word frequency features. Then, we reduced manual labeling work with the help of the TF-IDF algorithm. Finally, we proposed a Convolutional Neural Network (CNN) model using semantic and position information to generate the domain dictionary. [Results] Compared with the statistical learning method, our model improved the accuracy, recall and F1 values by 6%~9%. [Limitations] More research is needed to examine our method in other fields. [Conclusions] The proposed CNN-based method could effectively construct a dictionary for defective products.
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Received: 19 March 2020
Published: 04 December 2020
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Corresponding Authors:
Sun Ning
E-mail: sunninglx@163.com
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