This study investigated the effect of instruction that included graphic organizers on LDS seminary students’ ability to understand scriptural text and their ability to identify doctrines in scriptural text, utilizing a repeated measures, quasi-experimental design involving 209 ninth-grade student participants. The participants were randomly assigned by class to one of two treatment groups. Participants in the treatment group received instruction using graphic organizers with the standard curriculum and participants in the comparison group received instruction using only the standard curriculum. Three different measures were employed to measure the effectiveness of the graphic organizers intervention: (a) a multiple-choice test of LDS doctrines and principles; (b) an identifying doctrines and principles in text test; and (c) a student perception survey. Results of the ANOVA for the multiple-choice test indicated no significant difference between instructional groups for ability to recall facts from the class instruction and the class text, F (1, 205) = 1.60, p = .21, partial ή² = .21. Results of the ANOVA for the identifying doctrines and principles in text test, measuring transferability of the skills learned while studying the Doctrine and Covenants to a different text containing some of the same doctrines and principles, also indicated no significant difference between groups, F (1, 196) = 1.93, p = .17. The results for the student perception survey were positive; most students felt confident about their ability to comprehend scriptural text, but were slightly less confident about their ability to identify doctrines and principles in the text. The participants in this study were generally positive in their willingness to learn about and use graphic organizers. Results of this study indicated that graphic organizers did not significantly impact students’ ability to identify doctrines and principles in scriptural text or to learn concepts from scriptural text.

Eftersom vi idag lever i ett digitaliserat samhälle där litteracitet och teknologi alltmer hänger ihop, så även i skolan, har vi beslutat oss för att undersöka hur lärares uppfattningar om digital och analog läsning påverkar läsundervisningen i svenskämnet. Frågeställningarna som är tänkta att besvaras genom detta examensarbete är: Vilken betydelse har analog respektive digital läsning för elevers läsutveckling och vilken betydelse har lärares syn gällande läsning digitalt och analogt för läsundervisningen i svenskämnet? Frågeställningarna kommer att besvaras med hjälp av en kvalitativ metod, där materialet bygger på fem semistrukturerade intervjuer med mellanstadielärare i ämnet svenska. De teoretiska utgångspunkterna för studien är litteracitet och New Literacy. Materialet visar bland annat att den fysiska upplevelsen av analog läsning samt elevers motivation har en betydelse för läsutvecklingen och lärarnas planering av läsundervisningen i svenska. Till exempel förklarar samtliga deltagare hur de nästan enbart använder analoga texter vid undervisning av skönlitteratur och oftast använder digitala texter vid undervisning av faktatexter. En slutsats som kan dras av detta examensarbete är att digitala och analoga texter kompletterar varandra genom att de kan vara bra att använda för olika ändamål.

No description available.

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classiffication and string kernels. (authors' abstract)

Dada a grande quantidade e diversidade de dados textuais sendo criados diariamente, as aplicações do processo de Mineração de Textos são inúmeras e variadas. Nesse processo, a qualidade da solução final depende, em parte, do modelo de representação de textos adotado. Por se tratar de textos em língua natural, relações sintáticas e semânticas influenciam o seu significado. No entanto, modelos tradicionais de representação de textos se limitam às palavras, não sendo possível diferenciar documentos que possuem o mesmo vocabulário, mas que apresentam visões diferentes sobre um mesmo assunto. Nesse contexto, este trabalho foi motivado pela diversidade das aplicações da tarefa de classificação automática de textos, pelo potencial das representações no modelo espaço-vetorial e pela lacuna referente ao tratamento da semântica inerente aos dados em língua natural. O seu desenvolvimento teve o propósito geral de avançar as pesquisas da área de Mineração de Textos em relação à incorporação de aspectos semânticos na representação de coleções de documentos. Um mapeamento sistemático da literatura da área foi realizado e os problemas de classificação foram categorizados em relação à complexidade semântica envolvida. Aspectos semânticos foram abordados com a proposta, bem como o desenvolvimento e a avaliação de sete modelos de representação de textos: (i) gBoED, modelo que incorpora a semântica obtida por meio de conhecimento do domínio; (ii) Uni-based, modelo que incorpora a semântica por meio da desambiguação lexical de sentidos e hiperônimos de conceitos; (iii) SR-based Terms e SR-based Sentences, modelos que incorporam a semântica por meio de anotações de papéis semânticos; (iv) NASARIdocs, Babel2Vec e NASARI+Babel2Vec, modelos que incorporam a semântica por meio de desambiguação lexical de sentidos e embeddings de palavras e conceitos. Representações de coleções de documentos geradas com os modelos propostos e outros da literatura foram analisadas e avaliadas na classificação automática de textos, considerando datasets de diferentes níveis de complexidade semântica. As propostas gBoED, Uni-based, SR-based Terms e SR-based Sentences apresentam atributos mais expressivos e possibilitam uma melhor interpretação da representação dos documentos. Já as propostas NASARIdocs, Babel2Vec e NASARI+Babel2Vec incorporam, de maneira latente, a semântica obtida de embeddings geradas a partir de uma grande quantidade de documentos externos. Essa propriedade tem um impacto positivo na performance de classificação. / Text Mining applications are numerous and varied since a huge amount of textual data are created daily. The quality of the final solution of a Text Mining process depends, among other factors, on the adopted text representation model. Despite the fact that syntactic and semantic relations influence natural language meaning, traditional text representation models are limited to words. The use of such models does not allow the differentiation of documents that use the same vocabulary but present different ideas about the same subject. The motivation of this work relies on the diversity of text classification applications, the potential of vector space model representations and the challenge of dealing with text semantics. Having the general purpose of advance the field of semantic representation of documents, we first conducted a systematic mapping study of semantics-concerned Text Mining studies and we categorized classification problems according to their semantic complexity. Then, we approached semantic aspects of texts through the proposal, analysis, and evaluation of seven text representation models: (i) gBoED, which incorporates text semantics by the use of domain expressions; (ii) Uni-based, which takes advantage of word sense disambiguation and hypernym relations; (iii) SR-based Terms and SR-based Sentences, which make use of semantic role labels; (iv) NASARIdocs, Babel2Vec and NASARI+Babel2Vec, which take advantage of word sense disambiguation and embeddings of words and senses.We analyzed the expressiveness and interpretability of the proposed text representation models and evaluated their classification performance against different literature models. While the proposed models gBoED, Uni-based, SR-based Terms and SR-based Sentences have improved expressiveness, the proposals NASARIdocs, Babel2Vec and NASARI+Babel2Vec are latently enriched by the embeddings semantics, obtained from the large training corpus. This property has a positive impact on text classification performance.

This thesis focuses on two important aspects of non-native reading comprehension: text readability assessment, which estimates the reading difficulty of a given text for L2 learners, and learner summarisation assessment, which evaluates the quality of learner summaries to assess their reading comprehension. We approach both tasks as supervised machine learning problems and present automated assessment systems that achieve state-of-the-art performance. We first address the task of text readability assessment for L2 learners. One of the major challenges for a data-driven approach to text readability assessment is the lack of significantly-sized level-annotated data aimed at L2 learners. We present a dataset of CEFR-graded texts tailored for L2 learners and look into a range of linguistic features affecting text readability. We compare the text readability measures for native and L2 learners and explore methods that make use of the more plentiful data aimed at native readers to help improve L2 readability assessment. We then present a summarisation task for evaluating non-native reading comprehension and demonstrate an automated summarisation assessment system aimed at evaluating the quality of learner summaries. We propose three novel machine learning approaches to assessing learner summaries. In the first approach, we examine using several NLP techniques to extract features to measure the content similarity between the reading passage and the summary. In the second approach, we calculate a similarity matrix and apply a convolutional neural network (CNN) model to assess the summary quality using the similarity matrix. In the third approach, we build an end-to-end summarisation assessment model using recurrent neural networks (RNNs). Further, we combine the three approaches to a single system using a parallel ensemble modelling technique. We show that our models outperform traditional approaches that rely on exact word match on the task and that our best model produces quality assessments close to professional examiners.

No description available.

Automatically analysing the emotion in texts is in increasing interest in today&rsquo / s research fields. The aim is to develop a machine that can detect type of user&rsquo / s emotion from his/her text. Emotion classification of English texts is studied by several researchers and promising results are achieved. In this thesis, an emotion classification study on Turkish texts is introduced. To the best of our knowledge, this is the first study on emotion analysis of Turkish texts. In English there exists some well-defined datasets for the purpose of emotion classification, but we could not find datasets in Turkish suitable for this study. Therefore, another important contribution is the generating a new data set in Turkish for emotion analysis. The dataset is generated by combining two types of sources. Several classification algorithms are applied on the dataset and results are compared. Due to the nature of Turkish language, new features are added to the existing methods to improve the success of the proposed method.

abstract: Since the advent of the internet and even more after social media platforms, the explosive growth of textual data and its availability has made analysis a tedious task. Information extraction systems are available but are generally too specific and often only extract certain kinds of information they deem necessary and extraction worthy. Using data visualization theory and fast, interactive querying methods, leaving out information might not really be necessary. This thesis explores textual data visualization techniques, intuitive querying, and a novel approach to all-purpose textual information extraction to encode large text corpus to improve human understanding of the information present in textual data. This thesis presents a modified traversal algorithm on dependency parse output of text to extract all subject predicate object pairs from text while ensuring that no information is missed out. To support full scale, all-purpose information extraction from large text corpuses, a data preprocessing pipeline is recommended to be used before the extraction is run. The output format is designed specifically to fit on a node-edge-node model and form the building blocks of a network which makes understanding of the text and querying of information from corpus quick and intuitive. It attempts to reduce reading time and enhancing understanding of the text using interactive graph and timeline. / Dissertation/Thesis / Masters Thesis Software Engineering 2018

Dada a grande quantidade e diversidade de dados textuais sendo criados diariamente, as aplicações do processo de Mineração de Textos são inúmeras e variadas. Nesse processo, a qualidade da solução final depende, em parte, do modelo de representação de textos adotado. Por se tratar de textos em língua natural, relações sintáticas e semânticas influenciam o seu significado. No entanto, modelos tradicionais de representação de textos se limitam às palavras, não sendo possível diferenciar documentos que possuem o mesmo vocabulário, mas que apresentam visões diferentes sobre um mesmo assunto. Nesse contexto, este trabalho foi motivado pela diversidade das aplicações da tarefa de classificação automática de textos, pelo potencial das representações no modelo espaço-vetorial e pela lacuna referente ao tratamento da semântica inerente aos dados em língua natural. O seu desenvolvimento teve o propósito geral de avançar as pesquisas da área de Mineração de Textos em relação à incorporação de aspectos semânticos na representação de coleções de documentos. Um mapeamento sistemático da literatura da área foi realizado e os problemas de classificação foram categorizados em relação à complexidade semântica envolvida. Aspectos semânticos foram abordados com a proposta, bem como o desenvolvimento e a avaliação de sete modelos de representação de textos: (i) gBoED, modelo que incorpora a semântica obtida por meio de conhecimento do domínio; (ii) Uni-based, modelo que incorpora a semântica por meio da desambiguação lexical de sentidos e hiperônimos de conceitos; (iii) SR-based Terms e SR-based Sentences, modelos que incorporam a semântica por meio de anotações de papéis semânticos; (iv) NASARIdocs, Babel2Vec e NASARI+Babel2Vec, modelos que incorporam a semântica por meio de desambiguação lexical de sentidos e embeddings de palavras e conceitos. Representações de coleções de documentos geradas com os modelos propostos e outros da literatura foram analisadas e avaliadas na classificação automática de textos, considerando datasets de diferentes níveis de complexidade semântica. As propostas gBoED, Uni-based, SR-based Terms e SR-based Sentences apresentam atributos mais expressivos e possibilitam uma melhor interpretação da representação dos documentos. Já as propostas NASARIdocs, Babel2Vec e NASARI+Babel2Vec incorporam, de maneira latente, a semântica obtida de embeddings geradas a partir de uma grande quantidade de documentos externos. Essa propriedade tem um impacto positivo na performance de classificação. / Text Mining applications are numerous and varied since a huge amount of textual data are created daily. The quality of the final solution of a Text Mining process depends, among other factors, on the adopted text representation model. Despite the fact that syntactic and semantic relations influence natural language meaning, traditional text representation models are limited to words. The use of such models does not allow the differentiation of documents that use the same vocabulary but present different ideas about the same subject. The motivation of this work relies on the diversity of text classification applications, the potential of vector space model representations and the challenge of dealing with text semantics. Having the general purpose of advance the field of semantic representation of documents, we first conducted a systematic mapping study of semantics-concerned Text Mining studies and we categorized classification problems according to their semantic complexity. Then, we approached semantic aspects of texts through the proposal, analysis, and evaluation of seven text representation models: (i) gBoED, which incorporates text semantics by the use of domain expressions; (ii) Uni-based, which takes advantage of word sense disambiguation and hypernym relations; (iii) SR-based Terms and SR-based Sentences, which make use of semantic role labels; (iv) NASARIdocs, Babel2Vec and NASARI+Babel2Vec, which take advantage of word sense disambiguation and embeddings of words and senses.We analyzed the expressiveness and interpretability of the proposed text representation models and evaluated their classification performance against different literature models. While the proposed models gBoED, Uni-based, SR-based Terms and SR-based Sentences have improved expressiveness, the proposals NASARIdocs, Babel2Vec and NASARI+Babel2Vec are latently enriched by the embeddings semantics, obtained from the large training corpus. This property has a positive impact on text classification performance.