SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

This paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets were manually curated for the five languages with high inter-annotator agreements (consistently in the 0.9 ballpark). These were used for semi-automatic construction of ten cross-lingual datasets. 17 teams participated in the task, submitting 24 systems in subtask 1 and 14 systems in subtask 2. Results show that systems that combine statistical knowledge from text corpora, in the form of word embeddings, and external knowledge from lexical resources are best performers in both subtasks. More information can be found on the task website: http://alt.qcri.org/semeval2017/task2/

Explorando métodos non-supervisados para calcular a similitude semántica textual

Neste traballo preséntanse varios métodos non-supervisados para a detección da similitude semántica textual, os cales están baseados en modelos distribucionais e no parseado de dependencias. Os sistemas son avaliados mediante datasets empregados na ASSIN Shared Task, celebrada conxuntamente co PROPOR 2016. Os métodos máis básicos ofrecen un mellor comportamento que aqueles, mais complexos, que inclúen información sintáctico-semántica na análise das oracións. Por último, o uso de modelos distribucionais construidos automaticamente a partir de corpus ofrece resultados comparábeis ás estratexias que utilizan recursos léxicos externos construídos manualmente

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Expressions of psychological stress on Twitter: detection and characterisation

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Long-term psychological stress is a significant predictive factor for individual mental health and short-term stress is a useful indicator of an immediate problem. Traditional psychology studies have relied on surveys to understand reasons for stress in general and in specific contexts. The popularity and ubiquity of social media make it a potential data source for identifying and characterising aspects of stress. Previous studies of stress in social media have focused on users responding to stressful personal life events. Prior social media research has not explored expressions of stress in other important domains, however, including travel and politics. This thesis detects and analyses expressions of psychological stress in social media. So far, TensiStrength is the only existing lexicon for stress and relaxation scores in social media. Using a word-vector based word sense disambiguation method, the TensiStrength lexicon was modified to include the stress scores of the different senses of the same word. On a dataset of 1000 tweets containing ambiguous stress-related words, the accuracy of the modified TensiStrength increased by 4.3%. This thesis also finds and reports characteristics of a multiple-domain stress dataset of 12000 tweets, 3000 each for airlines, personal events, UK politics, and London traffic. A two-step method for identifying stressors in tweets was implemented. The first step used LDA topic modelling and k-means clustering to find a set of types of stressors (e.g., delay, accident). Second, three word-vector based methods - maximum-word similarity, context-vector similarity, and cluster-vector similarity - were used to detect the stressors in each tweet. The cluster vector similarity method was found to identify the stressors in tweets in all four domains better than machine learning classifiers, based on the performance metrics of accuracy, precision, recall, and f-measure. Swearing and sarcasm were also analysed in high-stress and no-stress datasets from the four domains using a Convolutional Neural Network and Multilayer Perceptron, respectively. The presence of swearing and sarcasm was higher in the high-stress tweets compared to no-stress tweets in all the domains. The stressors in each domain with higher percentages of swearing or sarcasm were identified. Furthermore, the distribution of the temporal classes (past, present, future, and atemporal) in high-stress tweets was found using an ensemble classifier. The distribution depended on the domain and the stressors. This study contributes a modified and improved lexicon for the identification of stress scores in social media texts. The two-step method to identify stressors follows a general framework that can be used for domains other than those which were studied. The presence of swearing, sarcasm, and the temporal classes of high-stress tweets belonging to different domains are found and compared to the findings from traditional psychology, for the first time. The algorithms and knowledge may be useful for travel, political, and personal life systems that need to identify stressful events in order to take appropriate action.European Union's Horizon 2020 research and innovation programme under grant agreement No 636160-2, the Optimum project (www.optimumproject.eu)

Technologies for extracting and analysing the credibility of health-related online content

The evolution of the Web has led to an improvement in information accessibility. This change has allowed access to more varied content at greater speed, but we must also be aware of the dangers involved. The results offered may be unreliable, inadequate, or of poor quality, leading to misinformation. This can have a greater or lesser impact depending on the domain, but is particularly sensitive when it comes to health-related content. In this thesis, we focus in the development of methods to automatically assess credibility. We also studied the reliability of the new Large Language Models (LLMs) to answer health questions. Finally, we also present a set of tools that might help in the massive analysis of web textual content

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction