Data provided by National Institute of Standards and Technology
Cross-language Information Retrieval (CLIR) has been studied at TREC and subsequent evaluation forums for more than twenty years, but recent advances in the application of deep learning to information retrieval (IR) warrant a new, large-scale effort that will enable exploration of classical and modern IR techniques for this task.
About this Dataset
Updated: 2025-04-06
Metadata Last Updated:
2023-02-01 00:00:00
Date Created:
N/A
Data Provided by:
Dataset Owner:
N/A
| Title | 2022 NeuCLIR Dataset |
|---|---|
| Description | Cross-language Information Retrieval (CLIR) has been studied at TREC and subsequent evaluation forums for more than twenty years, but recent advances in the application of deep learning to information retrieval (IR) warrant a new, large-scale effort that will enable exploration of classical and modern IR techniques for this task. |
| Modified | 2023-02-01 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | TREC text retrieval conference |
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"fn": "Ian Soboroff"
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"title": "2022 NeuCLIR Dataset",
"description": "Cross-language Information Retrieval (CLIR) has been studied at TREC and subsequent evaluation forums for more than twenty years, but recent advances in the application of deep learning to information retrieval (IR) warrant a new, large-scale effort that will enable exploration of classical and modern IR techniques for this task.",
"language": [
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"distribution": [
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"accessURL": "https:\/\/ir.nist.gov\/neuclir\/neuclir1.tar.gz",
"title": "2022 NeuCLIR Corpus"
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{
"accessURL": "https:\/\/trec.nist.gov\/data\/neuclir\/topics.0720.utf8.jsonl",
"title": "2022 NeuCLIR Topics"
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"accessURL": "https:\/\/ir-datasets.com\/neuclir.html",
"title": "2022 NeuCLIR Dataset"
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"theme": [
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