Adam Tsakalidis


2023

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Sequential Path Signature Networks for Personalised Longitudinal Language Modeling
Talia Tseriotou | Adam Tsakalidis | Peter Foster | Terence Lyons | Maria Liakata
Findings of the Association for Computational Linguistics: ACL 2023

Longitudinal user modeling can provide a strong signal for various downstream tasks. Despite the rapid progress in representation learning, dynamic aspects of modelling individuals’ language have only been sparsely addressed. We present a novel extension of neural sequential models using the notion of path signatures from rough path theory, which constitute graduated summaries of continuous paths and have the ability to capture non-linearities in trajectories. By combining path signatures of users’ history with contextual neural representations and recursive neural networks we can produce compact time-sensitive user representations. Given the magnitude of mental health conditions with symptoms manifesting in language, we show the applicability of our approach on the task of identifying changes in individuals’ mood by analysing their online textual content. By directly integrating signature transforms of users’ history in the model architecture we jointly address the two most important aspects of the task, namely sequentiality and temporality. Our approach achieves state-of-the-art performance on macro-average F1 score on the two available datasets for the task, outperforming or performing on-par with state-of-the-art models utilising only historical posts and even outperforming prior models which also have access to future posts of users.

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A Digital Language Coherence Marker for Monitoring Dementia
Dimitris Gkoumas | Adam Tsakalidis | Maria Liakata
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to learn the temporal logical consistency of utterances in short transcribed narratives and investigate a range of neural approaches. We compare such language coherence patterns between people with dementia and healthy controls and conduct a longitudinal evaluation against three clinical bio-markers to investigate the reliability of our proposed digital coherence marker. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer’s Disease and healthy controls. Moreover our analysis shows high association between the coherence marker and the clinical bio-markers as well as generalisability potential to other related conditions.

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Creation and evaluation of timelines for longitudinal user posts
Anthony Hills | Adam Tsakalidis | Federico Nanni | Ioannis Zachos | Maria Liakata
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

There is increasing interest to work with user generated content in social media, especially textual posts over time. Currently there is no consistent way of segmenting user posts into timelines in a meaningful way that improves the quality and cost of manual annotation. Here we propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user’s behaviour, based on their online posting activity. We also propose a novel framework for evaluating timelines and show its applicability in the context of two different social media datasets. Finally, we present a discussion of the linguistic content of highly ranked timelines.

2022

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Identifying Moments of Change from Longitudinal User Text
Adam Tsakalidis | Federico Nanni | Anthony Hills | Jenny Chim | Jiayu Song | Maria Liakata
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 focuses on either: (a) identifying individuals at risk or with a certain mental health condition given a batch of posts or (b) providing equivalent labels at the post level. A disadvantage of such work is the lack of a strong temporal component and the inability to make longitudinal assessments following an individual’s trajectory and allowing timely interventions. Here we define a new task, that of identifying moments of change in individuals on the basis of their shared content online. The changes we consider are sudden shifts in mood (switches) or gradual mood progression (escalations). We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines (18.7K posts). We have developed a variety of baseline models drawing inspiration from related tasks and show that the best performance is obtained through context aware sequential modelling. We also introduce new metrics for capturing rare events in temporal windows.

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Template-based Abstractive Microblog Opinion Summarization
Iman Munire Bilal | Bo Wang | Adam Tsakalidis | Dong Nguyen | Rob Procter | Maria Liakata
Transactions of the Association for Computational Linguistics, Volume 10

We introduce the task of microblog opinion summarization (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarization dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarizing news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, which usually involves selecting representative posts and thus favors extractive summarization models. To showcase the dataset’s utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarization models and achieve good performance, with the former outperforming the latter. We also show that fine-tuning is necessary to improve performance and investigate the benefits of using different sample sizes.

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Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
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
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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 . This year’s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual’s mood (dras- tic changes-‘Switches’- and gradual changes -‘Escalations’- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual – a continuation of the CLPsych 2019 Shared Task– where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).

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Unsupervised Opinion Summarisation in the Wasserstein Space
Jiayu Song | Iman Munire Bilal | Adam Tsakalidis | Rob Procter | Maria Liakata
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Opinion summarisation synthesises opinions expressed in a group of documents discussingthe same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus on opinion summarisation. To address these challenges we present WassOS, an unsupervised abstractive summarization model which makesuse of the Wasserstein distance. A Variational Autoencoder is first used to obtain the distribution of documents/posts, and the summary distribution is obtained as the Wasserstein barycenter. We create separate disentangled latent semantic and syntactic representations of the summary, which are fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments onmultiple datasets including reviews, Twitter clusters and Reddit threads show that WassOSalmost always outperforms the state-of-the-art on ROUGE metrics and consistently producesthe best summaries with respect to meaning preservation according to human evaluations.

2021

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Evaluation of Thematic Coherence in Microblogs
Iman Munire Bilal | Bo Wang | Maria Liakata | Rob Procter | Adam Tsakalidis
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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 how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.

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Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions
Adam Tsakalidis | Dana Atzil-Slonim | Asaf Polakovski | Natalie Shapira | Rivka Tuval-Mashiach | Maria Liakata
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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. Our NLP baseline outperforms a strong majority baseline by a large margin and captures client reported ruptures unidentified by therapists in 40% of such cases.

2020

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Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection
Adam Tsakalidis | Maria Liakata
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Semantic change detection concerns the task of identifying words whose meaning has changed over time. Current state-of-the-art approaches operating on neural embeddings detect the level of semantic change in a word by comparing its vector representation in two distinct time periods, without considering its evolution through time. In this work, we propose three variants of sequential models for detecting semantically shifted words, effectively accounting for the changes in the word representations over time. Through extensive experimentation under various settings with synthetic and real data we showcase the importance of sequential modelling of word vectors through time for semantic change detection. Finally, we compare different approaches in a quantitative manner, demonstrating that temporal modelling of word representations yields a clear-cut advantage in performance.

2019

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Mining the UK Web Archive for Semantic Change Detection
Adam Tsakalidis | Marya Bazzi | Mihai Cucuringu | Pierpaolo Basile | Barbara McGillivray
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

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. However, several obstacles make progress in the domain slow and difficult. These pertain primarily to the lack of well-established gold standard datasets, resources to study the problem at a fine-grained temporal resolution, and quantitative evaluation approaches. In this work, we aim to mitigate these issues by (a) releasing a new labelled dataset of more than 47K word vectors trained on the UK Web Archive over a short time-frame (2000-2013); (b) proposing a variant of Procrustes alignment to detect words that have undergone semantic shift; and (c) introducing a rank-based approach for evaluation purposes. Through extensive numerical experiments and validation, we illustrate the effectiveness of our approach against competitive baselines. Finally, we also make our resources publicly available to further enable research in the domain.

2017

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TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter
Bo Wang | Maria Liakata | Adam Tsakalidis | Spiros Georgakopoulos Kolaitis | Symeon Papadopoulos | Lazaros Apostolidis | Arkaitz Zubiaga | Rob Procter | Yiannis Kompatsiaris
Proceedings of the IJCNLP 2017, System Demonstrations

We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets. We describe the main elements of the system and illustrate its functionality with two examples of sentiment analysis of topics related to the 2017 UK general election.

2016

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Combining Heterogeneous User Generated Data to Sense Well-being
Adam Tsakalidis | Maria Liakata | Theo Damoulas | Brigitte Jellinek | Weisi Guo | Alexandra Cristea
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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. We describe the method for collecting the heterogeneous longitudinal data, how features are extracted to address missing information and differences in temporal alignment, and how the latter are combined to yield promising predictions of affect and well-being on the basis of widely used psychological scales. We achieve a coefficient of determination (R2) of 0.71-0.76 and a correlation coefficient of 0.68-0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.

2015

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WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition
Richard Townsend | Adam Tsakalidis | Yiwei Zhou | Bo Wang | Maria Liakata | Arkaitz Zubiaga | Alexandra Cristea | Rob Procter
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)