LIMSI@CLEF eHealth 2017 Task 2: Logistic Regression for Automatic Article Ranking

This paper describes the participation of the LIMSI-MIROR team at CLEF eHealth 2017, task 2. The task addresses the automatic ranking of articles in order to assist with the screening process of Diagnostic Test Accuracy (DTA) Systematic Reviews. We used a logistic regression classifier and handled class imbalance using a combination of class reweighting and undersampling. We also experimented with two strategies for relevance feedback. Our best run obtained an overall Average Precision of 0.179 and Work Saved over Sampling @95% Recall of 0.650. This run uses stochastic gradient descent for training but no feature selection or relevance feedback. We observe high performance variation within the queries in the test set. Nonetheless, our results suggest that automatic assistance is promising for ranking the DTA literature as it could reduce the screening workload for review writer by 65% on average

Plataforma software para análise textual de desordes mentais en Internet

[Resumo] A importancia dos métodos de prevención secundaria para os trastornos mentais é crucial xa que disto depende a calidade de vida futura das persoas que a sofren no presente. Os medios sociais poden favorecer un gran avance no desenvolvemento destes métodos debido a que son canles de comunicación nos que os usuarios e usuarias da Internet participan de forma activa e prevese que isto vaia aumentando co paso dos anos. O obxectivo principal deste proxecto é deseñar e implementar unha plataforma software que sirva de soporte aos especialistas da saúde mental, como psicólogos ou psiquiatras, para etiquetar aos suxeitos de maneira máis áxil, rápida e sinxela. A aplicación posibilitará a persoa usuaria procesar coleccións de documentos para ver as súas estadísticas, buscar documentos e realizar consultas a través delas. Tamén permitirá ao usuario ou usuaria analizar os resultados dos agrupamentos por similitude dos documentos ou termos que as conforman. Para poder alcanzar os obxectivos marcados eficientemente, decidiuse usar unha metodoloxía iterativa e incremental debido á flexibilidade e adaptación que aporta ás distintas situacións polas que un proxecto atravesa. Finalmente, conseguiuse unha plataforma software que cumpre cos requisitos especificados.[Abstract] Methods of secondary prevention for mental disorders are of crucial relevance because these depend the quality of future life of the people who are suffering mental diseases at the moment. Social media can improve develop these methods for the reason that it is a channel of communication where a lot of Internet users are involved and it is expected to increase over the years. The main goal of this project is to design and implement a software plataform to give support to mental health specialists, as psychologists or psychiatrists, to tag subjects agilely, quickly and easily. The application will allow the user process document collections to check their statistics, search documents and search queries in them. It will also allow the user to analyze the clustering results by similarity of the documents or terms that make them up. In order to achieve the goals set efficiently, it has been decided to use an iterative and incremental methodology for its flexibility and adaptation to the different situations that a project goes through. Eventually, it has been achieved an application that meets with the mentioned requirements.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2019/202

Deep Learning for Text Style Transfer: A Survey

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202