Η κατάλληλη οργάνωση του περιεχομένου ενός ιστοτόπου, έτσι ώστε να αυξάνεται η ευρεσιμότητα των πληροφοριών και να διευκολύνεται η επιτυχής ολοκλήρωση των τυπικών εργασιών των χρηστών, αποτελεί έναν από τους πρωταρχικούς στόχους των σχεδιαστών ιστοτόπων. Οι υπάρχουσες τεχνικές του πεδίου Αλληλεπίδρασης-Ανθρώπου Υπολογιστή που συνεισφέρουν στην επίτευξη αυτού του στόχου συχνά αγνοούνται εξαιτίας των απαιτήσεών τους σε χρονικούς και οικονομικούς πόρους. Ειδικότερα για ειδησεογραφικούς ιστοτόπους, τόσο το μέγεθος τους όσο και η καθημερινή προσθήκη και τροποποίηση των παρεχόμενων πληροφοριών, καθιστούν αναγκαία τη χρήση αποδοτικότερων τεχνικών για την οργάνωση του περιεχομένου τους. Στην εργασία αυτή διερευνούμε την αποτελεσματικότητα μίας μεθόδου, επονομαζόμενης AutoCardSorter, που έχει προταθεί στη βιβλιογραφία για την ημιαυτόματη κατηγοριοποίηση ιστοσελίδων, βάσει των σημασιολογικών συσχετίσεων του περιεχομένου τους, στο πλαίσιο οργάνωσης των πληροφοριών ειδησεογραφικών ιστοτόπων. Για το σκοπό αυτό διενεργήθηκαν πέντε συνολικά μελέτες, στις οποίες πραγματοποιήθηκε τόσο ποσοτική όσο και ποιοτική σύγκριση των κατηγοριοποιήσεων που προέκυψαν από συμμετέχοντες σε αντίστοιχες μελέτες ταξινόμησης καρτών ανοικτού και κλειστού τύπου, με τα αποτελέσματα της τεχνικής AutoCardSorter. Από την ανάλυση των αποτελεσμάτων προέκυψε ότι η AutoCardSorter παρήγαγε ομαδοποιήσεις άρθρων που βρίσκονται σε μεγάλη συμφωνία με αυτές των συμμετεχόντων στις μελέτες, αλλά με σημαντικά αποδοτικότερο τρόπο, επιβεβαιώνοντας προηγούμενες παρόμοιες μελέτες σε ιστοτόπους άλλων θεματικών κατηγοριών. Επιπρόσθετα, οι μελέτες έδειξαν ότι μία ελαφρώς τροποποιημένη εκδοχή της AutoCardSorter τοποθετεί νέα άρθρα σε προϋπάρχουσες κατηγορίες με αρκετά μικρότερο ποσοστό συμφωνίας συγκριτικά με τον τρόπο που επέλεξαν οι συμμετέχοντες. Η εργασία ολοκληρώνεται με την παρουσίαση κατευθύνσεων για την βελτίωση της αποτελεσματικότητας της AutoCardSorter, τόσο στο πλαίσιο οργάνωσης του περιεχομένου ειδησεογραφικών ιστοτόπων όσο και γενικότερα. / The proper structure of a website's content, so as to increase the findability of the information provided and to ease the typical user task-making, is one of the primary goals of website designers. The existing methods from the field of HCI that assist designers in this, are often neglected due to their high cost and human resources demanded. Even more so on News Sites, their size and the daily content updating call for improved and more efficient techniques. In this thesis we investigate the efficiency of a novel method, called AutoCardSorter, that has been suggested in bibliography for the semi-automatic content categorisation based on the semantic similarity of each webpage-content. To accomplish this we conducted five comparative studies in which the method was compared, to the primary alternatives of the classic Card Sorting method (open, closed). The analysis of the results showed that AutoCardSorter suggested article categories with high relavance to the ones suggested from a group of human subjects participating in the CardSort studies, although in a much more efficient way. This confirms the results of similar previous studies on websites of other themes (eg. travel, education). Moreover, the studies showed that a modified version of the method places articles under pre-existing categories with significant less relavance to the categorisation suggested by the participants. The thesis is concluded with the proposal of different ways to improve the proposed method's efficiency, both in the content of News Sites and in general.

Submitted by camilla martins (camillasmmartins@gmail.com) on 2017-01-27T15:50:37Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_AvaliacaoAutomaticaQuestoes.pdf: 5106074 bytes, checksum: c401d50ce5e666c52948ece7af20b2c3 (MD5) / Approved for entry into archive by Edisangela Bastos (edisangela@ufpa.br) on 2017-01-30T13:02:31Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_AvaliacaoAutomaticaQuestoes.pdf: 5106074 bytes, checksum: c401d50ce5e666c52948ece7af20b2c3 (MD5) / Made available in DSpace on 2017-01-30T13:02:31Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_AvaliacaoAutomaticaQuestoes.pdf: 5106074 bytes, checksum: c401d50ce5e666c52948ece7af20b2c3 (MD5) Previous issue date: 2016-02-05 / Este trabalho investiga o uso de um modelo usando Latent Semantic Analysis (LSA) na avaliação automática de respostas curtas, com média de 25 a 70 palavras, de questões discursivas. Com o surgimento de ambientes virtuais de aprendizagem, pesquisas sobre correção automática tornaram-se mais relevantes, pois permitem a correção mecânica com baixo custo para questões abertas. Além disso, a correção automática permite um feedback instantâneo e elimina o trabalho de correção manual. Isto possibilita criar turmas virtuais com grande quantidade de alunos (centenas ou milhares). Pesquisas sobre avaliação automática de textos estão sendo desenvolvidas desde a década de 60, mas somente na década atual estão alcançando a acurácia necessária para uso prático em instituições de ensino. Para que os usuários finais tenham confiança, o desafio de pesquisa é desenvolver sistemas de avaliação robustos e com acurácia próxima de avaliadores humanos. Apesar de alguns estudos apontarem nesta direção, existem ainda muitos pontos a serem explorados nas pesquisas. Um ponto é a utilização de bigramas com LSA, mesmo que não contribua muito com a acurácia, contribui com a robustez, que podemos definir como confiabilidade2, pois considera a ordem das palavras dentro do texto. Buscando aperfeiçoar um modelo LSA na direção de melhorar a acurácia e aumentar a robustez trabalhamos em quatro direções: primeira, incluímos bigramas de palavras no modelo LSA; segunda, combinamos modelos de co-ocorrência de unigrama e bigramas com uso de regressão linear múltipla; terceira, acrescentamos uma etapa de ajustes sobre a pontuação do modelo LSA baseados no número de palavras das respostas avaliadas; quarta, realizamos uma análise da distribuição das pontuações atribuídas pelo modelo LSA contra avaliadores humanos. Para avaliar os resultados comparamos a acurácia do sistema contra a acurácia de avaliadores humanos verificando o quanto o sistema se aproxima de um avaliador humano. Utilizamos um modelo LSA com cinco etapas: 1) pré- processamento, 2) ponderação, 3) decomposição a valores singulares, 4) classificação e 5) ajustes do modelo. Para cada etapa explorou-se estratégias alternativas que influenciaram na acurácia final. Nos experimentos obtivemos uma acurácia de 84,94% numa avaliação comparativa contra especialistas humanos, onde a correlação da acurácia entre especialistas humanos foi de 84,93%. No domínio estudado, a tecnologia de avaliação automática teve resultados próximos aos dos avaliadores humanos mostrando que esta alcançando um grau de maturidade para ser utilizada em sistemas de avaliação automática em ambientes virtuais de aprendizagem. / This work investigates the use of a model using Latent Semantic Analysis (LSA) In the automatic evaluation of short answers, with an average of 25 to 70 words, of questions Discursive With the emergence of virtual learning environments, research on Automatic correction have become more relevant as they allow the mechanical correction With low cost for open questions. In addition, automatic Feedback and eliminates manual correction work. This allows you to create classes With large numbers of students (hundreds or thousands). Evaluation research Texts have been developed since the 1960s, but only in the The current decade are achieving the necessary accuracy for practical use in teaching. For end users to have confidence, the research challenge is to develop Evaluation systems that are robust and close to human evaluators. despite Some studies point in this direction, there are still many points to be explored In the surveys. One point is the use of bigrasms with LSA, even if it does not contribute Very much with the accuracy, contributes with the robustness, that we can define as reliability2, Because it considers the order of words within the text. Seeking to perfect an LSA model In the direction of improving accuracy and increasing robustness we work in four directions: First, we include word bigrasms in the LSA model; Second, we combine models Co-occurrence of unigram and bigrams using multiple linear regression; third, We added a stage of adjustments on the LSA model score based on the Number of words of the responses evaluated; Fourth, we performed an analysis of the Of the scores attributed by the LSA model against human evaluators. To evaluate the We compared the accuracy of the system against the accuracy of human evaluators Verifying how close the system is to a human evaluator. We use a LSA model with five steps: 1) pre-processing, 2) weighting, 3) decomposition a Singular values, 4) classification and 5) model adjustments. For each stage it was explored Strategies that influenced the final accuracy. In the experiments we obtained An 84.94% accuracy in a comparative assessment against human Correlation among human specialists was 84.93%. In the field studied, the Evaluation technology had results close to those of the human evaluators Showing that it is reaching a degree of maturity to be used in Assessment in virtual learning environments. Google Tradutor para empresas:Google Toolkit de tradução para appsTradutor de sitesGlobal Market Finder.