构建无监督语义搜索流水线，精准检索事件原因相关文档。
A Semantic Search Pipeline for Causality-driven Adhoc Information Retrieval
- 融合语义与词法索引，多查询策略聚合结果
- 在CAIR-2021任务中领先传统与纯语义方法
- 适合需要挖掘事件因果关系的检索场景
我们提出一个针对因果驱动的临时信息检索（CAIR-2021）共享任务的无监督语义搜索流水线。该任务将传统信息检索扩展为支持检索可能包含查询事件原因的文档。成功系统需区分主题文档与包含事件因果描述的相关文档。本方法通过在语义和词法索引上聚合多种查询策略的结果，取得CAIR-2021排行榜首位，优于传统信息检索与纯语义嵌入方法。
原文摘要 · Abstract (English)
We present a unsupervised semantic search pipeline for the Causality-driven Adhoc Information Retrieval (CAIR-2021) shared task. The CAIR shared task expands traditional information retrieval to support the retrieval of documents containing the likely causes of a query event. A successful system must be able to distinguish between topical documents and documents containing causal descriptions of events that are causally related to the query event. Our approach involves aggregating results from multiple query strategies over a semantic and lexical index. The proposed approach leads the CAIR-2021 leaderboard and outperformed both traditional IR and pure semantic embedding-based approaches.
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