Dataset Card for LiMiT

Dataset Summary

Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The text in the dataset is in English (en).

Dataset Structure

Data Instances

Example of one instance in the dataset

{
    "id": 0,
    "motion": "yes",
    "motion_entities": [
        {
            "entity": "little boy",
            "start_index": 2
        },
        {
            "entity": "ball",
            "start_index": 30
        }
    ],
    "sentence": " A little boy holding a yellow ball walks by."
}

Data Fields

• id: intger index of the example
• motion: indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not
• motion_entities: A list of dicts with following keys
  • entity: the extracted entity in motion
  • start_index: index in the sentence for the first char of the entity text

Data Splits

The dataset is split into a train, and test split with the following sizes:

Dataset Creation

Curation Rationale

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Source Data

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Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@inproceedings{manotas-etal-2020-limit,
    title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
    author = "Manotas, Irene  and
      Vo, Ngoc Phuoc An  and
      Sheinin, Vadim",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
    doi = "10.18653/v1/2020.findings-emnlp.88",
    pages = "991--1000",
    abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
}

Contributions

Thanks to @patil-suraj for adding this dataset.