CNN-SM: Identifying Words on Defective Products with Sememe and Multi-features
You Xindong,Yuan Menglong,Zhang Le(),Lv Xueqiang
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China
[Objective] This paper proposes a CNN model based on the sememe and multi-features, aiming to improve the recognition accuracy of words on defected consumer products. [Methods] First, we created the model’s input with a distributed word vector fused with sememe. Then, we added part-of-speech features and randomly embedded word position vectors to the input. Finally, we removed the max pooling and increased the information contained in the depth vector output by the convolution kernel, which provided sufficient information for word classification. [Results] Compared with the CNN model only adding word position vectors, the proposed method improved the precision, recall and F1 values by 0.021, 0.002 and 0.012, respectively. [Limitations] We need to improve the polarity recognition of the same expression in different scenarios. [Conclusions] The sememe, part-of-speech, and the removal of pooling layer could improve the performance of model for domain word recognition.
游新冬, 袁梦龙, 张乐, 吕学强. CNN-SM：基于义原与多特征融合的消费品领域缺陷词识别模型*[J]. 数据分析与知识发现, 2022, 6(9): 77-85.
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