A Semantic Search Pipeline for Causality-driven Adhoc Information Retrieval
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917943037657088 |
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| author | Dalal, Dhairya Gupta, Sharmi Dev Binaei, Bentolhoda |
| author_facet | Dalal, Dhairya Gupta, Sharmi Dev Binaei, Bentolhoda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Semantic Search Pipeline for Causality-driven Adhoc Information Retrieval Dalal, Dhairya Gupta, Sharmi Dev Binaei, Bentolhoda Information Retrieval Artificial Intelligence Computation and Language 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. |
| title | A Semantic Search Pipeline for Causality-driven Adhoc Information Retrieval |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.01003 |