no code implementations • EMNLP 2020 • Adam Tsakalidis, Maria Liakata
Semantic change detection concerns the task of identifying words whose meaning has changed over time.
no code implementations • NAACL (CLPsych) 2021 • Adam Tsakalidis, Dana Atzil-Slonim, Asaf Polakovski, Natalie Shapira, Rivka Tuval-Mashiach, Maria Liakata
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.
no code implementations • NAACL (CLPsych) 2022 • Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata
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 .
no code implementations • 29 Jan 2024 • Jiayu Song, Jenny Chim, Adam Tsakalidis, Julia Ive, Dana Atzil-Slonim, Maria Liakata
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.
1 code implementation • 6 Dec 2023 • Talia Tseriotou, Ryan Sze-Yin Chan, Adam Tsakalidis, Iman Munire Bilal, Elena Kochkina, Terry Lyons, Maria Liakata
We present an open-source, pip installable toolkit, Sig-Networks, the first of its kind for longitudinal language modelling.
no code implementations • 14 Oct 2023 • Dimitris Gkoumas, Adam Tsakalidis, Maria Liakata
The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia.
1 code implementation • 10 Mar 2023 • Anthony Hills, Adam Tsakalidis, Federico Nanni, Ioannis Zachos, Maria Liakata
There is increasing interest to work with user generated content in social media, especially textual posts over time.
no code implementations • 27 Nov 2022 • Jiayu Song, Iman Munire Bilal, Adam Tsakalidis, Rob Procter, Maria Liakata
A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces.
no code implementations • 8 Aug 2022 • Iman Munire Bilal, Bo wang, Adam Tsakalidis, Dong Nguyen, Rob Procter, Maria Liakata
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.
no code implementations • ACL 2022 • Adam Tsakalidis, Federico Nanni, Anthony Hills, Jenny Chim, Jiayu Song, Maria Liakata
Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance.
no code implementations • 3 Sep 2021 • Dimitris Gkoumas, Bo wang, Adam Tsakalidis, Maria Wolters, Arkaitz Zubiaga, Matthew Purver, Maria Liakata
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.
1 code implementation • 2 Jul 2021 • Adam Tsakalidis, Pierpaolo Basile, Marya Bazzi, Mihai Cucuringu, Barbara McGillivray
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.
no code implementations • ACL 2021 • Iman Munire Bilal, Bo wang, Maria Liakata, Rob Procter, Adam Tsakalidis
Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence.
no code implementations • 5 Nov 2020 • Rabab Alkhalifa, Adam Tsakalidis, Arkaitz Zubiaga, Maria Liakata
In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task.
no code implementations • 28 Apr 2020 • Adam Tsakalidis, Maria Liakata
Semantic change detection concerns the task of identifying words whose meaning has changed over time.
no code implementations • RANLP 2019 • Adam Tsakalidis, Marya Bazzi, Mihai Cucuringu, Pierpaolo Basile, Barbara McGillivray
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.
no code implementations • 26 Aug 2018 • Adam Tsakalidis, Nikolaos Aletras, Alexandra I. Cristea, Maria Liakata
Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising.
no code implementations • 19 Jul 2018 • Adam Tsakalidis, Maria Liakata, Theo Damoulas, Alexandra I. Cristea
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.
no code implementations • IJCNLP 2017 • Bo Wang, Maria Liakata, Adam Tsakalidis, Spiros Georgakopoulos Kolaitis, Symeon Papadopoulos, Lazaros Apostolidis, Arkaitz Zubiaga, Rob Procter, Yiannis Kompatsiaris
We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets.
no code implementations • COLING 2016 • Adam Tsakalidis, Maria Liakata, Theo Damoulas, Brigitte Jellinek, Weisi Guo, Alex Cristea, ra
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.
1 code implementation • 25 Apr 2016 • Arkaitz Zubiaga, Alex Voss, Rob Procter, Maria Liakata, Bo wang, Adam Tsakalidis
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.