activity
20182022
most citedIdentifying Moments of Change from Longitudinal User Text

2 citations · 3 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL2022

Unsupervised Opinion Summarisation in the Wasserstein Space

Jiayu Song, Iman Munire Bilal, Adam Tsakalidis +2

Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation…

cs.CL20222 cited

Identifying Moments of Change from Longitudinal User Text

Adam Tsakalidis, Federico Nanni, Anthony Hills +3

Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic…

cs.CL2021

DUKweb: Diachronic word representations from the UK Web Archive corpus

Adam Tsakalidis, Pierpaolo Basile, Marya Bazzi +2

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 applica…

cs.CL2021

Evaluation of Thematic Coherence in Microblogs

Iman Munire Bilal, Bo Wang, Maria Liakata +2

Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is…

cs.CL2020

QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian

Rabab Alkhalifa, Adam Tsakalidis, Arkaitz Zubiaga +1

In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task. Our system focuses on using variations of training sets and different semantic de…

cs.CL20201 cited

Autoencoding Word Representations through Time for Semantic Change Detection

Adam Tsakalidis, Maria Liakata

Semantic change detection concerns the task of identifying words whose meaning has changed over time. The current state-of-the-art detects the level of semantic change in a word by…