Simplified Data Wrangling with ir_datasets
| Autor: | Nazli Goharian, Sean MacAvaney, Arman Cohan, Andrew Yates, Sergey Feldman, Doug Downey |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Focus (computing) Computer Science - Computation and Language Information retrieval Command-line interface Computer science business.industry Search engine indexing Python (programming language) Computer Science - Information Retrieval Documentation Data wrangling Benchmark (computing) The Internet business computer Computation and Language (cs.CL) Information Retrieval (cs.IR) computer.programming_language |
| Zdroj: | SIGIR SIGIR 2021: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Popis: | Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, there are numerous different data formats to work with. Even basic formats can have subtle dataset-specific nuances that need to be considered for proper use. To help mitigate these challenges, we introduce a new robust and lightweight tool (ir_datasets) for acquiring, managing, and performing typical operations over datasets used in IR. We primarily focus on textual datasets used for ad-hoc search. This tool provides both a Python and command line interface to numerous IR datasets and benchmarks. To our knowledge, this is the most extensive tool of its kind. Integrations with popular IR indexing and experimentation toolkits demonstrate the tool's utility. We also provide documentation of these datasets through the ir_datasets catalog: https://ir-datasets.com/. The catalog acts as a hub for information on datasets used in IR, providing core information about what data each benchmark provides as well as links to more detailed information. We welcome community contributions and intend to continue to maintain and grow this tool. Comment: SIGIR 2021 Resource |
| Databáze: | OpenAIRE |
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