计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210800197-5.doi: 10.11896/jsjkx.210800197
刘大为1, 车超1, 魏小鹏1,2
LIU Da-wei1, CHE Chao1, WEI Xiao-peng1,2
摘要： 在海关进出口商品文本信息中,往往会用不同的词语描述同一商品的特征,识别这些商品的特征同义词能更好地进行观点汇总,进而对同一类特征的商品进行涉税风险的防控。针对海关申报要素短语的特点,提出一种融合多层次信息的卷积神经网络模型,构建并训练了一个基于孪生和三级网络结构的Sentence-BERT,其对相近的要素短语具有更好的语义表示,弥补了word2vec短文本词嵌入特征离散稀疏的不足。利用多尺寸卷积核提取要素短语的不同特征。通过BiLSTM神经网络学习要素短语的语序信息,并利用注意力机制分配关键词权重。获得的全连接融合同义词语义特征和关键词特征,通过softmax层进行预测。实验证明,融合多层次信息的卷积模型比其他模型有更好的表现。
中图分类号:
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