二维码（扫一下试试看！） | | 面向信息与通信技术供应链网络画像构建的文本语义匹配方法 | | Text Semantic Matching Method for Information and Communication (ICT) Supply Chain Network Portrait Construction | | 投稿时间：2020-08-18 | | DOI：10.15918/j.tbit1001-0645.2020.132 | | 中文关键词:信息与通信技术供应链文本匹配卷积网络自注意力网络联合匹配模型 | | English Keywords:information and communication (ICT) supply chaintext matchingconvolutionself-attentionjoint matching model | | 基金项目:国家"二四二"信息安全计划项目（2017A149） | | | 摘要点击次数:267 | | 全文下载次数:155 | | 中文摘要: | | 针对信息与通信技术（information and communication，ICT）项目及产品数据种类多、形式复杂，难以提取其语义匹配信息，且现有句子级文本匹配模型对不同长度文本无差别编码会引入噪声导致匹配效果差的问题，本文提出一种融合局部和全局特征的实体-文档级联合匹配模型，利用TextCNN编码器提取实体级招投标项目和产品名称的局部信息，消除产品描述中与招投标项目无关信息的影响，再利用卷积-自注意力编码器提取文档级产品描述的局部和全局信息，最后结合实体级和文档级匹配信息进行决策.实验结果表明，招投标项目与供应商产品匹配映射准确率92%以上，方法可直接实际应用. | | English Summary: | | To extract information and communications technology (ICT) supply relationships from public text, it is necessary to achieve the matching of ICT bidding projects and supplier products accurately. However, the information on the supplier's official website related to the bidding project is distributed in the product name (entity) and product introduction (document), being difficult to establish its mapping association directly. Therefore, it is necessary to analyze the semantic of the text and to establish an accurate matching. Due to multi types and complex forms of ICT projects and product data, it is difficult to extract their matching semantic information. In addition, the existing text matching models can not differentiate the indiscrimination encoding in different levels of text, causing noise introduction and poor matching performance. To solve the problems, an entity-document level joint matching model was proposed. The model was arranged to use the TextCNN encoder to extract the local semantic information from the product name and the bidding project to eliminate the interference of irrelevant information in the product introduction. Then the CNN-SA encoder was used to extract the local and global information of the product introduction. Finally, the entity-level and document-level matching information was combined to make decisions. Experiment results show that the accuracy of matching mapping between bidding projects and supplier products can reach up to 92%. The method can be provided directly to practice application. | | 查看全文查看/发表评论下载PDF阅读器 | |
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