Sports Named Entity Recognition (Sports NER) Dataset

This dataset is designed for Named Entity Recognition (NER) tasks in the sports domain, focusing on extracting structured information from unstructured sports-related text. Named Entity Recognition is a fundamental task in Natural Language Processing (NLP) that identifies and classifies key elements such as names, locations, and events within text data :contentReference[oaicite:0]{index=0}.

The dataset contains annotated sentences covering a wide range of sports contexts, including match descriptions, player performances, tournament details, and rule-based scenarios.

Each text sample is labeled with domain-specific entity categories, including:

• Player Name
• Team Name
• Tournament Name
• Location
• Equipment Name
• Rules or Penalty
• Common Sports Terms (CST)
• Date and Time

These annotations enable the development of customized NER models tailored to sports analytics, where traditional general-purpose NER systems may not perform effectively due to domain-specific terminology :contentReference[oaicite:1]{index=1}.

The dataset can be used for various applications such as:

• Sports analytics and information extraction
• Intelligent sports news summarization
• Chatbots and question-answering systems
• Automated commentary analysis
• Knowledge graph construction in sports domain

This dataset is publicly available on Mendeley Data and can be accessed via the following DOI: https://data.mendeley.com/datasets/rcf4kbxtf8/2

License: Creative Commons Attribution 4.0 (CC BY 4.0)

If you use this dataset in your research or project, please cite the original source.