Keyword Embeddings for Query Suggestion

Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs. Current systems are very good at recommending lexical adaptations or spelling corrections to users' queries. However, they often struggle to suggest semantically related keywords given a user's query. The construction of a detailed query is crucial in some tasks, such as legal retrieval or academic search. In these scenarios, keyword suggestion methods are critical to guide the user during the query formulation. This paper proposes two novel models for the keyword suggestion task trained on scientific literature. Our techniques adapt the architecture of Word2Vec and FastText to generate keyword embeddings by leveraging documents' keyword co-occurrence. Along with these models, we also present a specially tailored negative sampling approach that exploits how keywords appear in academic publications. We devise a ranking-based evaluation methodology following both known-item and ad-hoc search scenarios. Finally, we evaluate our proposals against the state-of-the-art word and sentence embedding models showing considerable improvements over the baselines for the tasks

Plataforma web para la ejecución de modelos de clasificación como servicio

[Resumen] El exponencial crecimiento del volumen de datos almacenados en la nube en los últimos años, junto a la incipiente necesidad de analizarlos con el fin de obtener conocimiento a partir de los mismos, ha provocado la llamada revolución de los datos. En este contexto nace Modelab, una plataforma web para la ejecución de modelos de clasificación como servicio. La finalidad de esta plataforma es ofrecer a los usuarios una forma sencilla y rápida de realizar clasificaciones sobre grandes conjuntos de datos almacenados en la misma y ofrecer los resultados de estas clasificaciones de forma clara y concisa.[Abstract] The exponential growth of the volume of data stored in the cloud the last few years, together with the incipient need to analyse it in order to obtain knowledge from it, has led to the so-called data revolution. In this context, Modelab, a web platform for the execution of classification models as a service, was born. The purpose of this platform is to offer users a simple and fast way of carrying out classifications on large datasets stored on it and to offer the results of these classifications in a clear and concise way.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2019/202

Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Depression is one of the most prevalent mental health diseases. Although there are effective treatments, the main problem relies on providing early and effective risk detection. Medical experts use self-reporting questionnaires to elaborate their diagnosis, but these questionnaires have some limitations. Social stigmas and the lack of awareness often negatively affect the success of these self-report questionnaires. This article aims to describe techniques to automatically estimate the depression severity from users on social media. We explored the use of pre-trained language models over the subject’s writings. We addressed the task “Measuring the Severity of the Signs of Depression” of eRisk 2020, an initiative in the CLEF Conference. In this task, participants have to fill the Beck Depression Questionnaire (BDI-II). Our proposal explores the application of pre-trained Multiple-Choice Question Answering (MCQA) models to predict user’s answers to the BDI-II questionnaire using their posts on social media. These MCQA models are built over the BERT (Bidirectional Encoder Representations from Transformers) architecture. Our results showed that multiple-choice question answering models could be a suitable alternative for estimating the depression degree, even when small amounts of training data are available (20 users).This work was supported by projects RTI2018-093336-B-C22 (MCIU & ERDF), GPC ED431B 2019/03 (Xunta de Galicia & ERDF) and CITIC, which is financial supported by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the ERDF (80%) and Secretaría Xeral de Universidades (20%), (Ref ED431G 2019/01).Xunta de Galicia; ED431B 2019/03Xunta de Galicia; ED431G 2019/0

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculate

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculate