NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER.

The Dataset

We leverage the RapidNER framework to create NERsocial, a new dataset containing 153K tokens, 134K entities, and 99.4K sentences across six entity types: drinks, foods, hobbies, jobs, pets, sports

We augmented the six entity types above with three more types (by re-annotating the CoNLL2003 with PEOPLENAME, COUNTRY, ORGANIZATION).

NERsocial is a new named entity recognition dataset specifically designed for human-robot interaction (HRI) applications. It contains 99,448 sentences, 153,102 entity tokens, and 134,074 entities across six entity types that are crucial for social interaction: drinks, foods, hobbies, jobs, pets, and sports. The dataset was constructed using RapidNER, an efficient framework that combines knowledge graph extraction from Wikidata with text collection from multiple sources including Wikipedia, Reddit, and Stack Exchange.

The dataset's construction process was innovative and efficient, utilizing Elasticsearch for rapid annotation that reduced the time per sentence from 1 minute to 0.9 milliseconds. The texts were carefully selected from diverse sources: Wikipedia provided formal definitional content, while Reddit and Stack Exchange contributed conversational and interactive language patterns. The annotation quality was validated by human annotators, achieving a high inter-annotator agreement with a Fleiss Kappa score of 90.6% and Cohen's Kappa scores ranging from 88.3% to 92.9% between pairs of annotators.

When evaluated using state-of-the-art transformer models (BERT-base, RoBERTa-base, and DeBERTa-v3-base), NERsocial demonstrated strong performance with F1-scores above 95% across all models. The dataset particularly excels in robustness across different text domains, with models fine-tuned on NERsocial showing better transferability compared to those trained on similar datasets like WNUT. This makes NERsocial particularly valuable for developing NER systems that can handle both formal and informal communication in HRI applications.

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Data Format

{ 
'tokens': {"0": ["Poco", "Bueno", "was", "a", "American", "Quarter", "Horse", "stallion", "foaled", "April", "10", ",", "1944", "."], "1": ["Formal", "breeds", "often", "considered", "to", "be", "of", "the", "pit", "bull", "type", "include", "the", "American", "Pit", "Bull", "Terrier", ",", "American", "Staffordshire", "Terrier", ",", "American", "Bully", ",", "and", "Staffordshire", "Bull", "Terrier", "."], ... },
 'tags': {"0": ["O", "O", "O", "O", "B-PET", "I-PET", "I-PET", "O", "O", "O", "O", "O", "O", "O"], "1": ["O", "O", "O", "O", "O", "O", "O", "O", "B-PET", "I-PET", "O", "O", "O", "O", "B-PET", "I-PET", "I-PET", "O", "B-PET", "I-PET", "I-PET", "O", "B-PET", "I-PET", "O", "O", "B-PET", "I-PET", "I-PET", "O"], ...}
 }

The two dictionaries label2id and id2label are shown below:

Dictionary One: label2id

labels_to_ids = {
    'B-DRINK': 0,
    'B-FOOD': 1,
    'B-HOBBY': 2,
    'B-JOB': 3,
    'B-SPORT': 4,
    'I-DRINK': 5,
    'I-FOOD': 6,
    'I-HOBBY': 7,
    'I-JOB': 8,
    'I-SPORT': 9,
    'O': 10
}

Dictionary Two: id2label

ids_to_labels = {v: k for k, v in labels_to_ids.items()}

Usage and License Notices

The data is provided under an MIT license, so feel free to use it outside of research purposes.

To download the dataset, please use:

>>> from datasets import load_dataset

>>> dataset = load_dataset("atamiles/NERsocial")

Citation

If you use this dataset, please cite as follows:

@misc{atuhurra2024nersocialefficientnamedentity,
  title={NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER}, 
  author={Jesse Atuhurra and Hidetaka Kamigaito and Hiroki Ouchi and Hiroyuki Shindo and Taro Watanabe},
  year={2024},
  eprint={2412.09634},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2412.09634}, 
}