Search Results for author: Diana Maynard

Found 11 papers, 3 papers with code

Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus

no code implementations5 Jun 2020 Xingyi Song, Johann Petrak, Ye Jiang, Iknoor Singh, Diana Maynard, Kalina Bontcheva

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide.

Fact Checking General Classification

Helping Crisis Responders Find the Informative Needle in the Tweet Haystack

1 code implementation29 Jan 2018 Leon Derczynski, Kenny Meesters, Kalina Bontcheva, Diana Maynard

Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts.

General Classification Informativeness

Challenges of Evaluating Sentiment Analysis Tools on Social Media

no code implementations LREC 2016 Diana Maynard, Kalina Bontcheva

This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems.

Sentiment Analysis

Analysis of Named Entity Recognition and Linking for Tweets

no code implementations27 Oct 2014 Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, Kalina Bontcheva

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area.

Entity Disambiguation Language Identification +4

Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.

no code implementations LREC 2014 Diana Maynard, Mark Greenwood

Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too.

Opinion Mining Sarcasm Detection +1

Large Scale Semantic Annotation, Indexing and Search at The National Archives

no code implementations LREC 2012 Diana Maynard, Mark A. Greenwood

This paper describes a tool developed to improve access to the enormous volume of data housed at the UK's National Archives, both for the general public and for specialist researchers.

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