Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

313Citations
Citations of this article
177Readers
Mendeley users who have this article in their library.

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.

References Powered by Scopus

298Citations
477Readers
Get full text

Beyond the Zipf-Mandelbrot law in quantitative linguistics

174Citations
137Readers
Get full text

Discovering emerging entities with ambiguous names

94Citations
112Readers
Get full text

Cited by Powered by Scopus

A Survey on Deep Learning for Named Entity Recognition

913Citations
1591Readers
Get full text

FEW-NERD: A few-shot named entity recognition dataset

162Citations
210Readers
122Citations
324Readers

This article is free to access.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Derczynski, L., Nichols, E., Erp, M. V., & Limsopatham, N. (2017). Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition. In 3rd Workshop on Noisy User-Generated Text, W-NUT 2017 - Proceedings of the Workshop (pp. 140–147). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-4418

Readers over time

‘17‘18‘19‘20‘21‘22‘23‘24‘25010203040

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 74

76%

Researcher 15

15%

Lecturer / Post doc 5

5%

Professor / Associate Prof. 4

4%

Readers' Discipline

Tooltip

Computer Science 85

79%

Linguistics 10

9%

Engineering 8

7%

Business, Management and Accounting 4

4%

Save time finding and organizing research with Mendeley

Sign up for free
0