Improving Personalized Consumer Health Search

CLEF 2018 eHealth Consumer Health Search task aims to investigate the effectiveness of the information retrieval systems in providing health information to common health consumers. Compared to previous years, this year’s task includes five subtasks and adopts new data corpus and set of queries. This paper presents the work of University of Evora participating in two subtasks: IRtask-1 and IRtask-2. It explores the use of learning to rank techniques as well as query expan- sion approaches. A number of field based features are used for training a learning to rank model and a medical concept model proposed in previous work is re-employed for this year’s new task. Word vectors and UMLS are used as query expansion sources. Four runs were submitted to each task accordingly

Aprender línguas no âmbito dos EILC através da modalidade blended-learning

Organizar no país de acolhimento Erasmus Intensive Language Courses (EILC), cursos intensivos de língua para alunos do ensino superior em mobilidade, cursos esses financiados ou não pela União Europeia através das suas agências nacionais dos Programas de Aprendizagem ao Longo da Vida, não é uma tarefa fácil, em virtude da sua modalidade intensiva, da sua duração breve, do número elevado de horas letivas que têm e da heterogeneidade do público a que se destinam. Ao longo da nossa experiência profissional como docente de cursos de Português desta índole fomo-nos confrontando com problemas variados, que tentámos solucionar de forma cíclica, levando a cabo uma investigação do tipo investigação-ação, que nos permitiu descortinar pontos fracos, experimentar soluções, observar a reação do alunos envolvidos e reorientar o processo de ensino/aprendizagem com vista ao melhoramento do mesmo e à sua adequação aos indivíduos que estavam a frequentar a formação. São estas as questões que afloraremos neste artigo

The importance of subclasses of chitin synthase enzymes with myosin-like domains for the fitness of fungi

Acknowledgements TG and CF are funded by FEDER funds through the Operational Programme Competitiveness Factors – COMPETE and national funds by FCT – Foundation for Science and Technology under the strategic project UID/NEU/04539/2013. C.F. is a recipient of a postdoctoral fellowship from FCT-Fundação para a Ciência e Tecnologia (SFRH/BPD/63733/2009). NG is funded by The Wellcome Trust (080088, 086827, 075470, 099215 & 097377), the FungiBrain Marie Curie Network and the Medical Research Council (UK).Peer reviewedPostprin

A Survey on Object Classification using Convolutional Neural Networks

Object recognition has been one of the main tasks in computer vision. While feature detection and classification have been generally useful, an inquiry has been made to learning features suited to the task. One such method is the use of convolutional neural networks. This uses an architecture that combines elements of convolution, subsampling, and backpropagation. This paper gives an overview on the development, the use, and variations in using convolutional neural networks as an algorithm for object recognition tasks

Analysing part-of-speech for Portuguese text classification

This paper proposes and evaluates the use of linguistic in- formation in the pre-processing phase of text classification. We present several experiments evaluating the selection of terms based on different measures and linguistic knowledge. To build the classifier we used Sup- port Vector Machines (SVM), which are known to produce good results on text classification tasks. Our proposals were applied to two different datasets written in the Portuguese language: articles from a Brazilian newspaper (Folha de So Paulo) and juridical documents from the Portuguese Attorney General’s Office. The results show the relevance of part-of-speech information for the pre-processing phase of text classification allowing for a strong re- duction of the number of features needed in the text classification

The impact of NLP techniques in the multilabel text classification problem

Support Vector Machines have been used successfully to classify text documents into sets of concepts. However, typically, linguistic information is not being used in the classification process or its use has not been fully evaluated. We apply and evaluate two basic linguistic procedures (stop-word removal and stemming/lemmatization) to the multilabel text classification problem. These procedures are applied to the Reuters dataset and to the Portuguese juridical documents from Supreme Courts and Attorney General’s Office

Age and Gender Identification using Stacking for Classification⋆ Notebook for PAN at CLEF 2016

This paper presents our approach of identifying the profile of an unknown user based on the activities of known users. The aim of author profiling task of PAN@CLEF 2016 is cross-genre identification of the gender and age of an unknown user. This means training the system using the behavior of different users from one social media platform and identifying the profile of other user on some different platform. Instead of using single classifier to build the system we used a combination of different classifiers, also known as stacking. This approach allowed us explore the strength of all the classifiers and minimize the bias or error enforced by a single classifier

Enhancing a Portuguese text classifier using part-of-speech tags

Support Vector Machines have been applied to text classification with great success. In this paper, we apply and evaluate the impact of using part-of- speech tags (nouns, proper nouns, adjectives and verbs) as a feature selection procedure in a European Portuguese written dataset – the Portuguese Attorney General’s Office documents. From the results, we can conclude that verbs alone don’t have enough informa- tion to produce good learners. On the other hand, we obtain learners with equiva- lent performance and a reduced number of features (at least half) if we use specific part-of-speech tags instead of all words

Improving understandability in consumer health information search: Uevora @ 2016 fire chis

This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.Erasmus Mundus LEADER projec

Will i still study ... "When i'm sixty-four"? University experiences of mature students: challenges and obstacles

In 2006, the introduction of the Bologna Process in Portuguese Universities brought significant changes in the academic world, opening new doors to adult students in order to (re) join (again) in Higher Education. Returning’s to school has become, a difficult task due to a number of obstacles and difficulties that exist in the academic career (eg, reconciling family and professional lives, the understanding of specific programs contents, etc.). In this article we intend to explore the academic experiences of mature students when they decide to enter in Higher Education. What are main motivations and barriers during the academic career? What are the relationships established between teachers and other traditional students? What are the expectations for the future