Building Retrieval Systems for the ClueWeb22-B Corpus
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914669714735104 |
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| author | Mehrotra, Harshit Callan, Jamie Fan, Zhen |
| author_facet | Mehrotra, Harshit Callan, Jamie Fan, Zhen |
| contents | The ClueWeb22 dataset containing nearly 10 billion documents was released in 2022 to support academic and industry research. The goal of this project was to build retrieval baselines for the English section of the "super head" part (category B) of this dataset. These baselines can then be used by the research community to compare their systems and also to generate data to train/evaluate new retrieval and ranking algorithms. The report covers sparse and dense first stage retrievals as well as neural rerankers that were implemented for this dataset. These systems are available as a service on a Carnegie Mellon University cluster. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04357 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building Retrieval Systems for the ClueWeb22-B Corpus Mehrotra, Harshit Callan, Jamie Fan, Zhen Information Retrieval The ClueWeb22 dataset containing nearly 10 billion documents was released in 2022 to support academic and industry research. The goal of this project was to build retrieval baselines for the English section of the "super head" part (category B) of this dataset. These baselines can then be used by the research community to compare their systems and also to generate data to train/evaluate new retrieval and ranking algorithms. The report covers sparse and dense first stage retrievals as well as neural rerankers that were implemented for this dataset. These systems are available as a service on a Carnegie Mellon University cluster. |
| title | Building Retrieval Systems for the ClueWeb22-B Corpus |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2402.04357 |