A Semantic Search Pipeline for Causality-driven Adhoc Information Retrieval
This paper addresses the causal-aware ad-hoc retrieval (CAIR) task, where conventional information retrieval (IR) methods retrieve only topically relevant documents and fail to identify causal relationships between events. To tackle this, we propose the first unsupervised semantic search framework that disentangles causal intent from topical intent to enable causality-directed retrieval. Our approach synergistically integrates BERT-based semantic indexing with BM25 lexical indexing, and introduces three novel components: (i) event causal pattern expansion, (ii) counterfactual query reconstruction, and (iii) causality-aware re-ranking. Evaluated on the CAIR-2021 benchmark, our method achieves state-of-the-art performance, outperforming traditional IR baselines and pure semantic embedding approaches by over 12% in NDCG@10. This work provides the first empirical validation of the effectiveness and superiority of unsupervised paradigms for fine-grained causal retrieval.