Despina/semeval2010_task8

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Dataset Card for Transformed SemEval 2010 Task 8

Dataset Description

Dataset Summary

This dataset is released as part of the paper "Sub-Billion, Super-Frontier: Fine-Tuned Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction" (Christou & Tsoumakas, 2026) arXiv:2606.22606. The dataset is a transformed version of the SemEval 2010 Task 8 dataset (SemEvalWorkshop/sem_eval_2010_task_8). The original dataset is a standard benchmark for relation extraction and classification between nominal pairs.

This version preprocesses the original data into a more readily usable format with distinct features for entities and cleaned sentence text. Specifically:

• Entity text spans (, ) are extracted into separate fields.
• Special entity tags are removed from the main sentence text.
• Relation labels are provided as their full text names (e.g., "Cause-Effect(e1,e2)") instead of numerical IDs.
• Placeholder fields for entity types are included (set to "none").

The dataset contains training and testing splits as provided in the original SemEval task.

Supported Tasks and Leaderboards

• Relation Extraction: The primary task is to classify the semantic relationship between the two marked entities (entity1, entity2) within the given text.
• Few-Shot Learning: The dataset structure is suitable for N-shot evaluation protocols.
• Zero-Shot Learning: The dataset can be used for zero-shot evaluation, typically by leveraging the relation name strings as semantic information (requires further setup to define seen/unseen classes and map inputs to relation name embeddings).

Languages

The text in the dataset is in English (BCP-47: en).

Dataset Structure

Data Instances

A typical example from the dataset looks like this:

{
  'entity1': 'television',
  'entity2': 'programmes',
  'entity1Type': 'none',
  'entity2Type': 'none',
  'relation': 'Product-Producer(e2,e1)',
  'text': 'Most programmes have commercial breaks, but the quality of the television programmes depends on the channel.'
}

Data Fields

The dataset contains the following fields:

• entity1: (string) The text content of the first marked entity (head entity, originally marked with <e1>).
• entity2: (string) The text content of the second marked entity (tail entity, originally marked with <e2>).
• entity1Type: (string) Placeholder for the type of the first entity. Always set to "none" in this version as the original dataset does not provide explicit types.
• entity2Type: (string) Placeholder for the type of the second entity. Always set to "none" in this version.
• relation: (string) The text label representing the semantic relation between entity1 and entity2. Examples include "Cause-Effect(e1,e2)", "Entity-Destination(e1,e2)", "Product-Producer(e2,e1)", "Other". Note the directionality indicated in the label.
• text: (string) The full sentence text with the special entity marker tags (<e1>, </e1>, <e2>, </e2>) removed.

How to use

You can load the dataset using the Hugging Face datasets library:

from datasets import load_dataset

# Replace with the actual path on the Hugging Face Hub
dataset_name = "Despina/semeval2010_task8"
dataset = load_dataset(dataset_name)

# Access splits and features
print(dataset['train'][0])
# Expected Output (example):
# {
#  'entity1': 'television',
#  'entity2': 'programmes',
#  'entity1Type': 'none',
#  'entity2Type': 'none',
#  'relation': 'Product-Producer(e2,e1)',
#  'text': 'Most programmes have commercial breaks, but the quality of the television programmes depends on the channel.'
# }

Additional Information

Dataset Curators

This transformed version was generated based on user request using the Hugging Face datasets library. The original dataset was curated by the SemEval-2010 Task 8 organizers.

Licensing Information

The licensing terms for this transformed dataset are inherited from the original SemEvalWorkshop/sem_eval_2010_task_8 dataset. Please refer to the original dataset card or SemEval guidelines for specific licensing information (e.g., CC BY-SA or similar, but confirmation is recommended).

Citation Information

If you use this dataset in your work, please cite the original SemEval 2010 Task 8 paper, as also our paper:

@inproceedings{hendrickx-etal-2010-semeval,
    title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals",
    author = "Hendrickx, Iris  and
      Kim, Su Nam  and
      Kozareva, Zornitsa  and
      Nakov, Preslav  and
      {'O} S{'e}aghdha, Diarmuid  and
      Pad{'o}, Sebastian  and
      Pennacchiotti, Marco  and
      Romano, Lorenza  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation",
    month = jul,
    year = "2010",
    address = "Uppsala, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S10-1006",
    pages = "33--38",
}

@article{christou2026subbillion,
  title        = {Sub-Billion, Super-Frontier: Small Language Models Rival
                  Zero-Shot Frontier LLMs on General and Literary Relation Extraction},
  author       = {Christou, Despina and Tsoumakas, Grigorios},
  journal      = {arXiv preprint arXiv:2606.22606},
  year         = {2026},
  url          = {https://arxiv.org/abs/2606.22606}
}

Contributions

Thanks to the original SemEval 2010 Task 8 organizers and contributors, and the Hugging Face team for hosting the original dataset.