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Found 71 papers, 17 papers with code
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method
Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks.
Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions
We present the first work on automatically capturing alliance rupture in transcribed therapy sessions, trained on the text and self-reported rupture scores from both therapists and clients.
Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health .
Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection
Semantic change detection concerns the task of identifying words whose meaning has changed over time.
Assessing the Reasoning Abilities of ChatGPT in the Context of Claim Verification
Our study contributes to the growing body of research suggesting that ChatGPT's reasoning processes are unlikely to mirror human-like reasoning, and that LLMs need to be more rigorously evaluated to distinguish between hype and actual capabilities, especially in high-stakes real-world tasks such as claim verification.
Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media
We introduce a hybrid abstractive summarisation approach combining hierarchical VAE with LLMs (LlaMA-2) to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring.
Generating Zero-shot Abstractive Explanations for Rumour Verification
The task of rumour verification in social media concerns assessing the veracity of a claim on the basis of conversation threads that result from it.
Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
We present an open-source, pip installable toolkit, Sig-Networks, the first of its kind for longitudinal language modelling.
Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia
Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns.
A Digital Language Coherence Marker for Monitoring Dementia
The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia.
Automated clinical coding using off-the-shelf large language models
The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders.
Creation and evaluation of timelines for longitudinal user posts
There is increasing interest to work with user generated content in social media, especially textual posts over time.
PANACEA: An Automated Misinformation Detection System on COVID-19
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection.
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering
Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents.
Unsupervised Opinion Summarisation in the Wasserstein Space
A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces.
Template-based Abstractive Microblog Opinion Summarisation
We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain.
Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio
In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401, 174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner.
PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence
Work on social media rumour verification utilises signals from posts, their propagation and users involved.
Identifying Moments of Change from Longitudinal User Text
Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance.
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims.
Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes
We present work on summarising deliberative processes for non-English languages.
A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis
The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extra-linguistic information such as pen strokes and keystrokes.
Evaluation of Thematic Coherence in Microblogs
Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence.
CD\^2CR: Co-reference resolution across documents and domains
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents.
Citizen Participation and Machine Learning for a Better Democracy
The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations.
Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder
Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms.
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature.
CD2CR: Co-reference Resolution Across Documents and Domains
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents.
QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian
In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task.
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method
Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks.
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions
The purpose of this task is to determine the check-worthiness of tweets about COVID-19 to identify and prioritise tweets that need fact-checking.
Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews
Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders.
Measuring prominence of scientific work in online news as a proxy for impact
We find that Impact Case studies submitted to the UK Research Excellence Framework (REF) 2014 that refer to scientific papers mentioned in newspaper articles were awarded a higher score in the REF assessment.
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification
Question paraphrase identification is a key task in Community Question Answering (CQA) to determine if an incoming question has been previously asked.
tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection
Semantic similarity detection is a fundamental task in natural language understanding.
Estimating predictive uncertainty for rumour verification models
The inability to correctly resolve rumours circulating online can have harmful real-world consequences.
Autoencoding Word Representations through Time for Semantic Change Detection
Semantic change detection concerns the task of identifying words whose meaning has changed over time.
How we do things with words: Analyzing text as social and cultural data
Second, we hope to provide a set of best practices for working with thick social and cultural concepts.
Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets
Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty.
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour{'}s veracity.
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year's SemEval event.
Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum
Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising.
Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation
Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies.
HarriGT: A Tool for Linking News to Science
Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society.
All-in-one: Multi-task Learning for Rumour Verification
We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification.
Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers
We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers.
TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter
We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets.
ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners
Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners.
Incongruent Headlines: Yet Another Way to Mislead Your Readers
This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A).
Ranked #1 on
Stance Detection
on RumourEval
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Media is full of false claims.
Detection and Resolution of Rumours in Social Media: A Survey
Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i. e. pieces of information that are unverified at the time of posting.
TDParse: Multi-target-specific sentiment recognition on Twitter
Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned.
Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity
Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognised country.
Combining Heterogeneous User Generated Data to Sense Well-being
In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones.
Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories.
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
In this paper, we introduce the task of targeted aspect-based sentiment analysis.
Ranked #5 on
Aspect-Based Sentiment Analysis (ABSA)
on Sentihood
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest.
Using Gaussian Processes for Rumour Stance Classification in Social Media
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news.
Applying Core Scientific Concepts to Context-Based Citation Recommendation
The task of recommending relevant scientific literature for a draft academic paper has recently received significant interest.
Multi-label Annotation in Scientific Articles - The Multi-label Cancer Risk Assessment Corpus
Here, we present the Multi-CoreSC CRA corpus, a text corpus specific to the domain of cancer risk assessment (CRA), consisting of 50 full text papers, each of which contains sentences annotated with one or more CoreSCs.
Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario.
Leveraging Textual Features for Best Answer Prediction in Community-based Question Answering
In particular, we present a system, ACQUA [http://acqua. kmi. open. ac. uk], that can be installed onto the majority of browsers as a plugin.
