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Found 15 papers, 4 papers with code
Large Scale Semantic Annotation, Indexing and Search at The National Archives
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.
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.
Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too.
Analysis of Named Entity Recognition and Linking for Tweets
Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area.
Challenges of Evaluating Sentiment Analysis Tools on Social Media
This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems.
GATE-Time: Extraction of Temporal Expressions and Events
GATE is a widely used open-source solution for text processing with a large user community.
Comparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation
Preliminary analysis identifies clearly different attitudes on the same issue presented in different news sources.
Helping Crisis Responders Find the Informative Needle in the Tweet Haystack
Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts.
Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
This paper describes the participation of team {``}bertha-von-suttner{''} in the SemEval2019 task 4 Hyperpartisan News Detection task.
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
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.
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
First, we incorporate prompt learning into multimodal fake news detection.
Examining Temporal Bias in Abusive Language Detection
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right through to escalation to real-life violence and even death.
Dimensions of Online Conflict: Towards Modeling Agonism
Agonism plays a vital role in democratic dialogue by fostering diverse perspectives and robust discussions.
Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions
We present the development of a benchmark suite consisting of an annotation schema, training corpus and baseline model for Entity Recognition (ER) in job descriptions, published under a Creative Commons license.
