Dataset Card for "wnut_17"

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: http://noisy-text.github.io/2017/emerging-rare-entities.html
Repository: More Information Needed
Paper: More Information Needed
Point of Contact: More Information Needed
Size of downloaded dataset files: 0.76 MB
Size of the generated dataset: 1.66 MB
Total amount of disk used: 2.43 MB

Dataset Summary

WNUT 17: Emerging and Rare entity recognition

This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.

The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.

wnut_17

Size of downloaded dataset files: 0.76 MB
Size of the generated dataset: 1.66 MB
Total amount of disk used: 2.43 MB

An example of 'train' looks as follows.

{
    "id": "0",
    "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
    "tokens": ["@paulwalk", "It", "'s", "the", "view", "from", "where", "I", "'m", "living", "for", "two", "weeks", ".", "Empire", "State", "Building", "=", "ESB", ".", "Pretty", "bad", "storm", "here", "last", "evening", "."]
}

Data Fields

The data fields are the same among all splits.

wnut_17

id: a string feature.
tokens: a list of string features.
ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).

Data Splits Sample Size

name	train	validation	test
wnut_17	3394	1009	1287

@inproceedings{derczynski-etal-2017-results,
    title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
    author = "Derczynski, Leon  and
      Nichols, Eric  and
      van Erp, Marieke  and
      Limsopatham, Nut",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4418",
    doi = "10.18653/v1/W17-4418",
    pages = "140--147",
    abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
                Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization),
                but recall on them is a real problem in noisy text - even among annotators.
                This drop tends to be due to novel entities and surface forms.
                Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'}
                hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities,
                and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the
                ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}

Contributions

Thanks to @thomwolf, @lhoestq, @stefan-it, @lewtun, @jplu for adding this dataset.

Datasets:

leondz
/

wnut_17

Dataset Card for "wnut_17"

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks

Languages

Dataset Structure

Data Instances

wnut_17

Data Fields

wnut_17

Data Splits Sample Size

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