GKTR:一种融合图卷积拓扑特征和关键词特征的工程咨询报告检索模型

GKTR Retrieval Model for Engineering Consulting Reports with Graph Convolution Topological and Keyword Features

[目的]针对现有检索方法语义特征提取不充分的问题,提出一种融合图卷积拓扑特征和关键词特征的工程咨询报告检索模型。[方法]构建面向工程咨询报告的文本检索语料集,将语料传入BERT模型得到上下文向量,并通过图卷积网络和深度交互匹配模型得到第一个匹配得分;同时将段落关键词通过Word2Vec模型得到向量映射,与标题进行相似度计算得到第二个匹配得分。取两个匹配得分的平均值得到最终的匹配得分。[结果]GKTR联合多种文本交互匹配模型,相较于联合排序模型CEDR在P@20指标上最高提升3.06个百分点。[局限]实验数据主要来源于大型国企工程咨询公司的工程咨询报告,在其他领域中的效果有待验证。[结论]GKTR模型在面向工程咨询报告的文本检索语料库上,能够有效提升文本检索的效果。

[Objective]This paper proposes a text retrieval model for engineering consulting reports that combines graph convolution topological and keyword features.It addresses the insufficient semantic feature extraction issues in existing retrieval methods.[Methods]First,we built a text retrieval corpus of engineering consulting reports.Then,we fed the corpus into a BERT model to obtain contextual vectors.Third,we obtained the first matching score through a graph convolutional network and a deep interactive matching model.We also mapped the paragraph keywords to vectors using a Word2Vec model and calculated their similarity scores with the titles to obtain the second matching score.Finally,we got their final matching score by averaging the two matching scores.[Results]Compared with the joint ranking model CEDR,our new model was up to 3.06%higher in the P@20 metric.[Limitations]The data was mainly from engineering consulting reports of a large state-owned company,which needs to be expanded.[Conclusions]The GKTR model could effectively improve text retrieval for engineering consulting reports.