Hyponym Expansion Based on Concept Chains and Graph Attention Networks
[Objective]This study addresses the issue of topic drift in hyponym expansion in interactive retrieval scenarios.[Methods]We used a graph attention network to encode the relationship graph's nodes between concept chains and texts.Then,we modeled the concept chains through the word interaction processes and obtained the relationship graph based on character co-occurrence relations.By introducing the attention mechanism,our method overcomes the problem of losing query scenario information in traditional text encoding processes.[Results]The proposed method improved the F1 score by 2.0％compared to the best method,PRGC.[Limitations]The proposed method was designed for interactive scenarios and depended on the interactive data quality.[Conclusions]The proposed model effectively integrates the concept chains'structural and semantic features into text features.It also calculates attention for concept chains and candidate texts,reducing the loss of scenario topic information during encoding and mitigating the topic drift problem.
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