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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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Proceedings ArticleDOI

BERTweet: A pre-trained language model for English Tweets

TL;DR: BERweet as discussed by the authors is the first large-scale pre-trained language model for English Tweets, having the same architecture as BERT-base and is trained using the RoBERTa pre-training procedure.
Proceedings ArticleDOI

FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP

TL;DR: The core idea of the FLAIR framework is to present a simple, unified interface for conceptually very different types of word and document embeddings, which effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” variousembeddings with little effort.
Journal ArticleDOI

A Survey on Deep Learning for Named Entity Recognition

TL;DR: A comprehensive review on existing deep learning techniques for NER is provided in this paper, where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
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The Pushshift Reddit Dataset

TL;DR: The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.
References
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Proceedings Article

Efficient Named Entity Annotation through Pre-empting

TL;DR: A technique for reducing the amount of entity-less text examined by annotators, which is called "preempting", is demonstrated and evaluated in a crowdsourcing scenario, where it provides downstream performance improvements for the same size corpus.
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