No description available.

[pt] Dados acadêmicos (e.g., Teses, Dissertações) englobam aspectos de toda uma sociedade, bem como seu conhecimento científico. Neles, há uma riqueza de informações a ser explorada por modelos computacionais, e que podem ser positivos para sociedade. Os modelos de aprendizado de máquina, em especial, possuem uma crescente necessidade de dados para treinamento, que precisam ser estruturados e de tamanho considerável. Seu uso na área de processamento de linguagem natural é pervasivo nas mais diversas tarefas. Este trabalho realiza o esforço de coleta, construção, análise do maior corpus acadêmico conhecido na língua portuguesa. Foram treinados modelos de vetores de palavras, bag-of-words e transformer. O modelo transformer BERTAcadêmico apresentou os melhores resultados, com 77 por cento de f1-score na classificação da Grande Área de conhecimento e 63 por cento de f1-score na classificação da Área de conhecimento nas categorizações de Teses e Dissertações. É feita ainda uma análise semântica do corpus acadêmico através da modelagem de tópicos, e uma visualização inédita das áreas de conhecimento em forma de clusters. Por fim, é apresentada uma aplicação que faz uso dos modelos treinados, o SucupiraBot. / [en] Academic data (i.e., Thesis, Dissertation) encompasses aspects of a whole society, as well as its scientific knowledge. There is a wealth of information to be explored by computational models, and that can be positive for society. Machine learning models in particular, have an increasing need for training data, that are efficient and of considerable size. Its use in the area of natural language processing (NLP) is pervasive in many different tasks. This work makes the effort of collecting, constructing, analyzing and training of models for the biggest known academic corpus in the Portuguese language. Word embeddings, bag of words and transformers models have been trained. The Bert-Academico has shown the better result, with 77 percent of f1-score in Great area of knowledge and 63 percent in knowledge area classification of Thesis and Dissertation. A semantic analysis of the academic corpus is made through topic modelling, and an unprecedented visualization of the knowledge areas is presented. Lastly, an application that uses the trained models is showcased, the SucupiraBot.

Each day, millions of people ask questions and search for answers on the World Wide Web. Due to this, the Internet has grown to a world wide database of questions and answers, accessible to almost everyone. Since this database is so huge, it is hard to find out whether a question has been answered or even asked before. As a consequence, users are asking the same questions again and again, producing a vicious circle of new content which hides the important information. One platform for questions and answers are Web forums, also known as discussion boards. They present discussions as item streams where each item contains the contribution of one author. These contributions contain questions and answers in human readable form. People use search engines to search for information on such platforms. However, current search engines are neither optimized to highlight individual questions and answers nor to show which questions are asked often and which ones are already answered. In order to close this gap, this thesis introduces the \\emph{Effingo} system. The Effingo system is intended to extract forums from around the Web and find question and answer items. It also needs to link equal questions and aggregate associated answers. That way it is possible to find out whether a question has been asked before and whether it has already been answered. Based on these information it is possible to derive the most urgent questions from the system, to determine which ones are new and which ones are discussed and answered frequently. As a result, users are prevented from creating useless discussions, thus reducing the server load and information overload for further searches. The first research area explored by this thesis is forum data extraction. The results from this area are intended be used to create a database of forum posts as large as possible. Furthermore, it uses question-answer detection in order to find out which forum items are questions and which ones are answers and, finally, topic detection to aggregate questions on the same topic as well as discover duplicate answers. These areas are either extended by Effingo, using forum specific features such as the user graph, forum item relations and forum link structure, or adapted as a means to cope with the specific problems created by user generated content. Such problems arise from poorly written and very short texts as well as from hidden or distributed information.

Nous proposons une approche pour détecter les sujets, les communautés d'intérêt non disjointes,l'expertise, les tendances et les activités dans des sites où le contenu est généré par les utilisateurs et enparticulier dans des forums de questions-réponses tels que StackOverFlow. Nous décrivons d'abordQASM (Questions & Réponses dans des médias sociaux), un système basé sur l'analyse de réseauxsociaux pour gérer les deux principales ressources d’un site de questions-réponses: les utilisateurs et lecontenu. Nous présentons également le vocabulaire QASM utilisé pour formaliser à la fois le niveaud'intérêt et l'expertise des utilisateurs. Nous proposons ensuite une approche efficace pour détecter lescommunautés d'intérêts. Elle repose sur une autre méthode pour enrichir les questions avec un tag plusgénéral en cas de besoin. Nous comparons trois méthodes de détection sur un jeu de données extrait dusite populaire StackOverflow. Notre méthode basée sur le se révèle être beaucoup plus simple et plusrapide, tout en préservant la qualité de la détection. Nous proposons en complément une méthode pourgénérer automatiquement un label pour un sujet détecté en analysant le sens et les liens de ses mots-clefs.Nous menons alors une étude pour comparer différents algorithmes pour générer ce label. Enfin, nousétendons notre modèle de graphes probabilistes pour modéliser conjointement les sujets, l'expertise, lesactivités et les tendances. Nous le validons sur des données du monde réel pour confirmer l'efficacité denotre modèle intégrant les comportements des utilisateurs et la dynamique des sujets / We propose an approach to detect topics, overlapping communities of interest, expertise, trends andactivities in user-generated content sites and in particular in question-answering forums such asStackOverFlow. We first describe QASM (Question & Answer Social Media), a system based on socialnetwork analysis to manage the two main resources in question-answering sites: users and contents. Wealso introduce the QASM vocabulary used to formalize both the level of interest and the expertise ofusers on topics. We then propose an efficient approach to detect communities of interest. It relies onanother method to enrich questions with a more general tag when needed. We compared threedetection methods on a dataset extracted from the popular Q&A site StackOverflow. Our method basedon topic modeling and user membership assignment is shown to be much simpler and faster whilepreserving the quality of the detection. We then propose an additional method to automatically generatea label for a detected topic by analyzing the meaning and links of its bag of words. We conduct a userstudy to compare different algorithms to choose the label. Finally we extend our probabilistic graphicalmodel to jointly model topics, expertise, activities and trends. We performed experiments with realworlddata to confirm the effectiveness of our joint model, studying the users’ behaviors and topicsdynamics