Submitted by Fabricia Fialho Reginato (fabriciar) on 2015-07-01T23:00:34Z No. of bitstreams: 1 EvandroFlores.pdf: 1289007 bytes, checksum: 44450c63dc59c23ca86b3e4fdbdcea30 (MD5) / Made available in DSpace on 2015-07-01T23:00:34Z (GMT). No. of bitstreams: 1 EvandroFlores.pdf: 1289007 bytes, checksum: 44450c63dc59c23ca86b3e4fdbdcea30 (MD5) Previous issue date: 2014 / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / GVDASA / A rápida evolução das tecnologias de informação e comunicação tem possibilitado o desenvolvimento de modalidades de ensino e educação, tais como a Educação a Distância, capazes de alcançar pessoas anteriormente impossibilitadas de frequentar o ensino superior. Um aspecto importante destas modalidades é o amplo uso de recursos de mediação digital, sendo que estes podem gerar um grande volume de dados o qual, por vezes, não é viável para utilização proveitosa de forma manual pelos professores envolvidos nesta interação. Este contexto gera a necessidade e oportunidade de definição de ferramentas que possam atuar para automatizar parte deste trabalho. Uma destas possibilidades é a verificação de correção de respostas textuais, onde o objetivo é identificar vinculações entre amostras textuais que podem ser, por exemplo, diferentes respostas textuais a uma pergunta. Embora sejam utilizadas com bons resultados, as técnicas atualmente aplicadas a este problema apresentam deficiências ou características que diminuem sua precisão ou adequação em diversos contextos. Poucos trabalhos são capazes de realizar a vinculação textual caso seja alterada a flexão verbal, outros não são capazes de identificar informações importantes ou em que posição na frase as informações se encontram. Além disso, poucos trabalhos são adaptados para a língua portuguesa. Este trabalho propõe um modelo de reconhecimento de vinculação textual baseado em regras linguísticas e informações morfossintáticas voltado para ambientes virtuais de ensino e aprendizagem, que busca contornar estes problemas apresentando uma nova abordagem através do uso combinado da análise sintática, morfológica, regras linguísticas, detecção da flexão de voz, tratamento de negação e do uso de sinônimos. O trabalho também apresenta um protótipo desenvolvido para avaliar o modelo proposto. Ao final são apresentados os resultados obtidos, que até o momento são promissores, permitindo a identificação da vinculação textual de diferentes amostras textuais com precisão e flexibilidade relevantes. / The fast evolution of information and communication technologies has enabled the development of modalities of teaching and learning, such as distance education, that allow to reach people previously unable to attend higher education. An important aspect of these modalities is the extensive use of digital mediation resources. These resources can generate a large volume of data that sometimes is not feasible for beneficial manual use by the teachers involved in this interaction. In this context there is a necessity and opportunity for defining tools and approaches that can act to automate part of this work. One of these possibilities is the verification of textual responses correctness, where the goal is to identify linkages between textual samples, which can be, for example, different textual answer to a question. Although presenting good results, techniques currently applied to this problem have deficiencies or characteristics that decrease their accuracy or suitability in several contexts. Few studies are able to perform textual entailment in case the verbal inflection was changed; others are not able to identify important information or position in the sentence where the information is found. Moreover, few works are adapted to Portuguese. This work proposes a model to recognition of textual entailment based on linguistic rules, which seeks to overcome these problems by presenting a new approach through the combined use of syntactic analysis, morphology, linguistic rules, detection of the bending voice, treatment of denial and the use of synonyms. This work also presents a prototype developed to evaluate the model proposed herein. The end results, which are promising, allow the identification of textual linking of different textual samples accurately and with flexibility.

Les problématiques abordées dans ma thèse sont de définir une adaptation unifiée entre la sélection des documents et les stratégies de recherche de la réponse à partir du type des documents et de celui des questions, intégrer la solution au système de Questions-Réponses (QR) RITEL du LIMSI et évaluer son apport. Nous développons et étudions une méthode basée sur une approche de Recherche d’Information pour la sélection de documents en QR. Celle-ci s’appuie sur un modèle de langue et un modèle de classification binaire de texte en catégorie pertinent ou non pertinent d’un point de vue QR. Cette méthode permet de filtrer les documents sélectionnés pour l’extraction de réponses par un système QR. Nous présentons la méthode et ses modèles, et la testons dans le cadre QR à l’aide de RITEL. L’évaluation est faite en français en contexte web sur un corpus de 500 000 pages web et de questions factuelles fournis par le programme Quaero. Celle-ci est menée soit sur des documents complets, soit sur des segments de documents. L’hypothèse suivie est que le contenu informationnel des segments est plus cohérent et facilite l’extraction de réponses. Dans le premier cas, les gains obtenus sont faibles comparés aux résultats de référence (sans filtrage). Dans le second cas, les gains sont plus élevés et confortent l’hypothèse, sans pour autant être significatifs. Une étude approfondie des liens existant entre les performances de RITEL et les paramètres de filtrage complète ces évaluations. Le système de segmentation créé pour travailler sur des segments est détaillé et évalué. Son évaluation nous sert à mesurer l’impact de la variabilité naturelle des pages web (en taille et en contenu) sur la tâche QR, en lien avec l’hypothèse précédente. En général, les résultats expérimentaux obtenus suggèrent que notre méthode aide un système QR dans sa tâche. Cependant, de nouvelles évaluations sont à mener pour rendre ces résultats significatifs, et notamment en utilisant des corpus de questions plus importants. / This thesis aims at defining a unified adaptation of the document selection and answer extraction strategies, based on the document and question types, in a Question-Answering (QA) context. The solution is integrated in RITEL (a LIMSI QA system) to assess the contribution. We develop and investigate a method based on an Information Retrieval approach for the selection of relevant documents in QA. The method is based on a language model and a binary model of textual classification in relevant or irrelevant category. It is used to filter unusable documents for answer extraction by matching lists of a priori relevant documents to the question type automatically. First, we present the method along with its underlying models and we evaluate it on the QA task with RITEL in French. The evaluation is done on a corpus of 500,000 unsegmented web pages with factoid questions provided by the Quaero program (i.e. evaluation at the document level or D-level). Then, we evaluate the methodon segmented web pages (i.e. evaluation at the segment level or S-level). The idea is that information content is more consistent with segments, which facilitates answer extraction. D-filtering brings a small improvement over the baseline (no filtering). S-filtering outperforms both the baseline and D-filtering but not significantly. Finally, we study at the S-level the links between RITEL’s performances and the key parameters of the method. In order to apply the method on segments, we created a system of web page segmentation. We present and evaluate it on the QA task with the same corpora used to evaluate our document selection method. This evaluation follows the former hypothesis and measures the impact of natural web page variability (in terms of size and content) on RITEL in its task. In general, the experimental results we obtained suggest that our IR-based method helps a QA system in its task, however further investigations should be conducted – especially with larger corpora of questions – to make them significant.