Datasets:
limit

	---
	annotations_creators:
	- crowdsourced
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-sa-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- extended\|net-activities-captions
	- original
	task_categories:
	- token-classification
	- text-classification
	task_ids:
	- multi-class-classification
	- named-entity-recognition
	paperswithcode_id: limit
	pretty_name: LiMiT
	dataset_info:
	features:
	- name: id
	dtype: int32
	- name: sentence
	dtype: string
	- name: motion
	dtype: string
	- name: motion_entities
	list:
	- name: entity
	dtype: string
	- name: start_index
	dtype: int32
	splits:
	- name: train
	num_bytes: 3064208
	num_examples: 23559
	- name: test
	num_bytes: 139742
	num_examples: 1000
	download_size: 4214925
	dataset_size: 3203950
	---

	# Dataset Card for LiMiT

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: -
	- Repository: [github](https://github.com/ilmgut/limit_dataset)
	- Paper: [LiMiT: The Literal Motion in Text Dataset](https://www.aclweb.org/anthology/2020.findings-emnlp.88/)
	- Leaderboard: N/A
	- Point of Contact: [More Information Needed]

	### 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:

	\| \| train \| validation \|
	\| ----- \|------:\|-----------:\|
	\| Number of examples \| 23559 \| 1000 \|

	## Dataset Creation

	### Curation Rationale

	[More Information Needed]

	### Source Data

	[More Information Needed]

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	[More Information Needed]

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	[More Information Needed]

	### 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](https://github.com/patil-suraj) for adding this dataset.