Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023

MentalRiskES is a novel challenge that proposes to solve problems related to early risk detection for the Spanish language. The objective is to detect, as soon as possible, Telegram users who show signs of mental disorders considering different tasks. Task 1 involved the users' detection of eating disorders, Task 2 focused on depression detection, and Task 3 aimed at detecting an unknown disorder. These tasks were divided into subtasks, each one defining a resolution approach. Our research group participated in subtask A for Tasks 1 and 2: a binary classification problem that evaluated whether the users were positive or negative. To solve these tasks, we proposed models based on Transformers followed by a decision policy according to criteria defined by an early detection framework. One of the models presented an extended vocabulary with important words for each task to be solved. In addition, we applied a decision policy based on the history of predictions that the model performs during user evaluation. For Tasks 1 and 2, we obtained the second-best performance according to rankings based on classification and latency, demonstrating the effectiveness and consistency of our approaches for solving early detection problems in the Spanish language.Comment: In Iberian Languages Evaluation Forum (IberLEF 2023), Ja\'en, Spai

Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023

The CLEF eRisk Laboratory explores solutions to different tasks related to risk detection on the Internet. In the 2023 edition, Task 1 consisted of searching for symptoms of depression, the objective of which was to extract user writings according to their relevance to the BDI Questionnaire symptoms. Task 2 was related to the problem of early detection of pathological gambling risks, where the participants had to detect users at risk as quickly as possible. Finally, Task 3 consisted of estimating the severity levels of signs of eating disorders. Our research group participated in the first two tasks, proposing solutions based on Transformers. For Task 1, we applied different approaches that can be interesting in information retrieval tasks. Two proposals were based on the similarity of contextualized embedding vectors, and the other one was based on prompting, an attractive current technique of machine learning. For Task 2, we proposed three fine-tuned models followed by decision policy according to criteria defined by an early detection framework. One model presented extended vocabulary with important words to the addressed domain. In the last task, we obtained good performances considering the decision-based metrics, ranking-based metrics, and runtime. In this work, we explore different ways to deploy the predictive potential of Transformers in eRisk tasks.Comment: In Conference and Labs of the Evaluation Forum (CLEF 2023), Thessaloniki, Greec

Making sense of nonsense : Integrated gradient-based input reduction to improve recall for check-worthy claim detection

Analysing long text documents of political discourse to identify check-worthy claims (claim detection) is known to be an important task in automated fact-checking systems, as it saves the precious time of fact-checkers, allowing for more fact-checks. However, existing methods use black-box deep neural NLP models to detect check-worthy claims, which limits the understanding of the model and the mistakes they make. The aim of this study is therefore to leverage an explainable neural NLP method to improve the claim detection task. Specifically, we exploit well known integrated gradient-based input reduction on textCNN and BiLSTM to create two different reduced claim data sets from ClaimBuster. We observe that a higher recall in check-worthy claim detection is achieved on the data reduced by BiLSTM compared to the models trained on claims. This is an important remark since the cost of overlooking check-worthy claims is high in claim detection for fact-checking. This is also the case when a pre-trained BERT sequence classification model is fine-tuned on the reduced data set. We argue that removing superfluous tokens using explainable NLP could unlock the true potential of neural language models for claim detection, even though the reduced claims might make no sense to humans. Our findings provide insights on task formulation, design of annotation schema and data set preparation for check-worthy claim detection.publishedVersio

Experimental IR meets multilinguality, multimodality, and interaction : 10th International Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9–12, 2019, proceedings

This book constitutes the refereed proceedings of the 10th International Conference of the CLEF Association, CLEF 2019, held in Lugano, Switzerland, in September 2019. The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 7 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 30 submissions. This year, many contributions tackle the social networks with the detection of stances or early identification of depression signs on Twitter in a cross-lingual context. Further this volume presents 7 “best of the labs” papers which were reviewed as a full paper submission with the same review criteria. The labs represented scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. In addition to this, 9 benchmarking labs reported results of their yearlong activities in overview talks and lab sessions

Report from Dagstuhl Seminar 23031: Frontiers of Information Access Experimentation for Research and Education

This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specifically focused on developing more responsible experimental practices leading to more valid results, both for research as well as for scientific education. The seminar brought together experts from various sub-fields of information access, namely IR, RS, NLP, information science, and human-computer interaction to create a joint understanding of the problems and challenges presented by next generation information access systems, from both the research and the experimentation point of views, to discuss existing solutions and impediments, and to propose next steps to be pursued in the area in order to improve not also our research methods and findings but also the education of the new generation of researchers and developers. The seminar featured a series of long and short talks delivered by participants, who helped in setting a common ground and in letting emerge topics of interest to be explored as the main output of the seminar. This led to the definition of five groups which investigated challenges, opportunities, and next steps in the following areas: reality check, i.e. conducting real-world studies, human-machine-collaborative relevance judgment frameworks, overcoming methodological challenges in information retrieval and recommender systems through awareness and education, results-blind reviewing, and guidance for authors.Comment: Dagstuhl Seminar 23031, report