scispace - formally typeset
Open AccessProceedings ArticleDOI

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

TLDR
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 and to evaluate the ability of participating entries to detect and classify novel and emerging named entities in noisy text.
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.

read more

Citations
More filters
Journal ArticleDOI

HealthE: Classifying Entities in Online Textual Health Advice

TL;DR: A new annotated dataset, HealthE, consisting of 6,756 health advice, which has a more granular label space compared to existing medical NER corpora and contains annotation for diverse health phrases and introduces a new health entity classification model, EP S-BERT, which leverages textual context patterns in the classi-cation of entity classes.

Named Entity Recognition for Social Media Text

Yaxi Zhang
TL;DR: This thesis aims to perform named entity recognition for English social media texts with Named Entity Recognition (NER) applied in many NLP tasks as an important preprocessing procedure.
Journal ArticleDOI

Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition

Tieyun Qian
- 13 Feb 2023 - 
TL;DR: TadNER as discussed by the authors proposed a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names and then constructed more accurate and stable prototypes by jointly exploiting support samples and type names as references.
Journal ArticleDOI

NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension

TL;DR: In this paper , a NER-to-MRC model is proposed to transform the NER task into a form suitable for the model to solve with MRC in an efficient manner.
Journal ArticleDOI

PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search

TL;DR: In this article , the authors proposed a prompting method for few-shot NER via k nearest neighbor search, which enables the model to fine-tune with only the support set.
References
More filters
Proceedings ArticleDOI

The Stanford CoreNLP Natural Language Processing Toolkit

TL;DR: The design and use of the Stanford CoreNLP toolkit is described, an extensible pipeline that provides core natural language analysis, and it is suggested that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
Book

Naming and Necessity

TL;DR: In this paper, the authors make a connection between the mind-body problem and the so-called "identity thesis" in analytic philosophy, which has wide-ranging implications for other problems in philosophy that traditionally might be thought far-removed.
Proceedings ArticleDOI

Introduction to the CoNLL-2003 shared task: language-independent named entity recognition

TL;DR: The CoNLL-2003 shared task on NER as mentioned in this paper was the first NER task with language-independent named entity recognition (NER) data sets and evaluation method, and a general overview of the systems that participated in the task and their performance.
Posted Content

NLTK: The Natural Language Toolkit

TL;DR: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware that covers symbolic and statistical natural language processing.
Related Papers (5)