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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.

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Citations
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Journal ArticleDOI

tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation

TL;DR: The authors propose a structured annotation framework that makes their annotations fully exposed and not buried in unstructured code, which can save time for future dataset preprocessings, even if they do not use their framework.
Journal ArticleDOI

Prompt-Based Metric Learning for Few-Shot NER

Yanru Chen, +2 more
- 08 Nov 2022 - 
TL;DR: This article proposed a simple method to largely improve metric learning for NER: multiple prompt schemas are designed to enhance label semantics, and a novel architecture to effectively combine multiple prompt-based representations.
Proceedings ArticleDOI

Learning In-context Learning for Named Entity Recognition

TL;DR: In this paper , the authors propose an in-context learning-based NER approach, which can effectively inject incontext NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
References
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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.
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