%A Peng Chen,Lv Xueqiang,Sun Ning,Zang Le,Jiang Zhaocai,Song Li %T Building Phrase Dictionary for Defective Products with Convolutional Neural Network %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2020.0214 %P 112-120 %V 4 %N 11 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4956.shtml} %8 2020-11-25 %X

[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.