
# 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.
task_categories:token-classification task_categories:text-classification task_ids:multi-class-classification task_ids:named-entity-recognition annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|net-activities-captions source_datasets:original language:en license:cc-by-sa-4.0 region:us
{"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}}
TAGS
#task_categories-token-classification #task_categories-text-classification #task_ids-multi-class-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|net-activities-captions #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 

Dataset Card for LiMiT
======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: -
* Repository: github
* Paper: LiMiT: The Literal Motion in Text Dataset
* Leaderboard: N/A
* Point of Contact:


### 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


### Languages


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


Dataset Structure
-----------------


### Data Instances


Example of one instance in the dataset


### 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


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


Thanks to @patil-suraj for adding this dataset.



### 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 ### Languages


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


Dataset Structure
----------------- ### Data Instances


Example of one instance in the dataset ### 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @patil-suraj for adding this dataset.
TAGS
#task_categories-token-classification #task_categories-text-classification #task_ids-multi-class-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|net-activities-captions #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 
 ### 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 ### Languages


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


Dataset Structure
----------------- ### Data Instances


Example of one instance in the dataset ### 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @patil-suraj for adding this dataset.
passage: TAGS
#task_categories-token-classification #task_categories-text-classification #task_ids-multi-class-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|net-activities-captions #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 
### 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### Languages


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


Dataset Structure
-----------------### Data Instances


Example of one instance in the dataset### 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### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases