Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …

We present BERTweet, the first public large-scale pre-trained language model for English
Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is …

Twitter was an integral part of Donald Trump's communication platform during his 2016
campaign. Although its topical content has been examined by researchers and the media …

Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text
is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We …

Human civilizations have performed the art of writing across continents and over different
time periods. In order to speed up the writing process, the art of shorthand (brachygraphy) …

This paper presents the results of the two shared tasks associated with W-NUT 2015:(1) a
text normalization task with 10 participants; and (2) a named entity tagging task with 8 …

Abstract Machine learning, including neural network techniques, have been applied to
virtually every domain in natural language processing. One problem that has been …

Twitter provides access to large volumes of data in real time, but is notoriously noisy,
hampering its utility for NLP. In this article, we target out-of-vocabulary words in short text …

Recent research efforts have shown that neural architectures can be effective in
conventional information extraction tasks such as named entity recognition, yielding state-of …

Microblog normalisation methods often utilise complex models and struggle to differentiate
between correctly-spelled unknown words and lexical variants of known words. In this …