Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval | |
Yu XL(于雪莉); Xu WZ(许伟志)![]() ![]() ![]() ![]() | |
2021-04-22 | |
会议名称 | the 30th Web Conference |
会议日期 | 2021-4-19 ~ 2021-4-23 |
会议地点 | Ljubljana, Slovenia |
摘要 | The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based ap- proaches have been proposed to solve such problem and gained lots of attention. However, we argue that they are inherently based on local word sequences, ignoring the subtle long-distance document- level word relationships. To solve the problem, we explicitly model the document-level word relationship through the graph structure, capturing the subtle information via graph neural networks. In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level. Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hi- erarchical matching signals simultaneously. We validate the effects of GHRM over two representative ad-hoc retrieval benchmarks, the comprehensive experiments and results demonstrate its superiority over state-of-the-art methods. |
收录类别 | EI |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52152 |
专题 | 模式识别实验室 |
作者单位 | 中科院自动化所 |
推荐引用方式 GB/T 7714 | Yu XL,Xu WZ,Cui ZY,et al. Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval[C],2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
[WWW2021] Graph-base(920KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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