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Found 22 papers, 4 papers with code
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.
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 .
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.
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.
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.
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.
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.
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.
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.
DUKweb: Diachronic word representations from the UK Web Archive corpus
Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications.
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.
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.
Autoencoding Word Representations through Time for Semantic Change Detection
Semantic change detection concerns the task of identifying words whose meaning has changed over time.
Mining the UK Web Archive for Semantic Change Detection
Semantic change detection (i. e., identifying words whose meaning has changed over time) started emerging as a growing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social science.
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.
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.
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.
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.
