Search Results for author:
Found 48 papers, 19 papers with code
SemEval-2022 Task 4: Patronizing and Condescending Language Detection
This paper presents an overview of Task 4 at SemEval-2022, which was focused on detecting Patronizing and Condescending Language (PCL) towards vulnerable communities.
CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection
This paper describes the experiments ran for SemEval-2022 Task 2, subtask A, zero-shot and one-shot settings for idiomaticity detection.
Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions
Interpreting patient case descriptions has emerged as a challenging problem for biomedical NLP, where the aim is typically to predict diagnoses, to recommended treatments, or to answer questions about cases more generally.
WiDe-analysis: Enabling One-click Content Moderation Analysis on Wikipedia's Articles for Deletion
Content moderation in online platforms is crucial for ensuring activity therein adheres to existing policies, especially as these platforms grow.
Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models
Large language models (LLMs) have been shown to propagate and amplify harmful stereotypes, particularly those that disproportionately affect marginalised communities.
CHEW: A Dataset of CHanging Events in Wikipedia
We introduce CHEW, a novel dataset of changing events in Wikipedia expressed in naturally occurring text.
Hoaxpedia: A Unified Wikipedia Hoax Articles Dataset
Hoaxes are a recognised form of disinformation created deliberately, with potential serious implications in the credibility of reference knowledge resources such as Wikipedia.
Construction Artifacts in Metaphor Identification Datasets
Metaphor identification aims at understanding whether a given expression is used figuratively in context.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks.
RAGAS: Automated Evaluation of Retrieval Augmented Generation
We introduce RAGAs (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines.
WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset
A fundamental challenge in the current NLP context, dominated by language models, comes from the inflexibility of current architectures to 'learn' new information.
3D-EX : A Unified Dataset of Definitions and Dictionary Examples
Definitions are a fundamental building block in lexicography, linguistics and computational semantics.
Modelling Commonsense Properties using Pre-Trained Bi-Encoders
Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding.
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media.
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers
Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP.
Deriving Disinformation Insights from Geolocalized Twitter Callouts
In particular, the analysis in centered on Twitter and disinformation for three European languages: English, French and Spanish.
Probing Pre-Trained Language Models for Disease Knowledge
Pre-trained language models such as ClinicalBERT have achieved impressive results on tasks such as medical Natural Language Inference.
BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?
Analogies play a central role in human commonsense reasoning.
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings
While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context.
Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification
We describe the system submitted to SemEval-2020 Task 6, Subtask 1.
Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities
In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescending towards vulnerable communities (e. g. refugees, homeless people, poor families).
Overview of CAPITEL Shared Tasks at IberLEF 2020: Named Entity Recognition and Universal Dependencies Parsing
We present the results of the CAPITEL-EVAL shared task, held in the context of the IberLEF 2020 competition series.
Towards Preemptive Detection of Depression and Anxiety in Twitter
Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life.
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
The experimental landscape in natural language processing for social media is too fragmented.
Ranked #3 on
Sentiment Analysis
on TweetEval
Modelling Semantic Categories using Conceptual Neighborhood
Unfortunately, meaningful regions can be difficult to estimate, especially since we often have few examples of individuals that belong to a given category.
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages are integrated together.
Cross-Lingual Natural Language Inference
Cross-Lingual Word Embeddings
+3
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning
Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language.
Relational Word Embeddings
While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited.
Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection
This paper summarizes our contribution to the Hyperpartisan News Detection task in SemEval 2019.
Learning Cross-lingual Embeddings from Twitter via Distant Supervision
Cross-lingual embeddings represent the meaning of words from different languages in the same vector space.
Interpretable Emoji Prediction via Label-Wise Attention LSTMs
Human language has evolved towards newer forms of communication such as social media, where emojis (i. e., ideograms bearing a visual meaning) play a key role.
Improving Cross-Lingual Word Embeddings by Meeting in the Middle
Cross-lingual word embeddings are becoming increasingly important in multilingual NLP.
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors
For example, by examining clusters of relation vectors, we observe that relational similarities can be identified at a more abstract level than with traditional word vector differences.
Natural Language Processing for Music Knowledge Discovery
Today, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago.
The Interplay between Lexical Resources and Natural Language Processing
Incorporating linguistic, world and common sense knowledge into AI/NLP systems is currently an important research area, with several open problems and challenges.
SemEval 2018 Task 2: Multilingual Emoji Prediction
This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018.
Syntactically Aware Neural Architectures for Definition Extraction
Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search.
SemEval-2018 Task 9: Hypernym Discovery
This paper describes the SemEval 2018 Shared Task on Hypernym Discovery.
Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes
Videogame streaming platforms have become a paramount example of noisy user-generated text.
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning
WordNet is probably the best known lexical resource in Natural Language Processing.
DefExt: A Semi Supervised Definition Extraction Tool
We present DefExt, an easy to use semi supervised Definition Extraction Tool.
