When children encounter new subjects in school, they are also faced with new ways of using language. Learning science thus means learning the language of science, and writing is one of the ways this is accomplished. The present study investigates writing in natural sciences in grades 5 and 8 in Swedish schools. Major theoretical influences for these investigations are found within the socio-cultural, dialogical and social semiotic perspectives on language use. The study is based on texts written by 97 students, interviews around these texts and observations from 16 different classroom practices. Writing is seen as a situated practice; therefore analysis is carried out of the activities surrounding the texts. The student texts are analysed in terms of genre and in relation to their abstraction, density and use of expansions. This analysis shows among other things that the texts show increasing abstraction and density with increasing age, whereas the text structure and the use of expansions do not increase. It is also argued that a central point in school writing must be the students’ way of talking about their texts. Analysis of interviews with the students is thus carried out in terms of text movability. The results from this analysis indicate that students find it difficult to talk about their texts. They find it hard to express the main content of the text, as well as to discuss it’s function and potential readers. Previous studies argue that writing constitutes a potential for learning. In the material studied in this thesis, this potential learning tool is not used to any large extent. To be able to participate in natural sciences in higher levels, students need to take part in practices where the specialized language of natural science is used in writing as well as in speech.

The aim of this study was to quantitatively measure refutation text's power for conceptual change while qualitatively discovering students' preference of refutation or expository text structures. This study also sought to examine if religious interest levels predict conceptual change. Participants for this study were 9th, 10th-, 11th-, and 12th-grade seminary students from the private religious educational system of The Church of Jesus Christ of Latter-day Saints (LDS). The study was conducted in two sessions. Session 1 involved pretesting, interventions, and posttesting. Session 2 involved delayed posttesting and participant interviews. Results were predominately measured quantitatively with some qualitative interview analysis added to enrich the study. This research study provides insight into the refutation text effects in LDS religious education. Results of the study showed significant differences in conceptual change between participants reading refutation texts and those reading expository texts. In every case, the refutation text group performed higher on posttests than did the expository group. Results also showed participant preference toward refutation text structures. Furthermore, the study found significant correlations that verify topic interest as a possible predictor of conceptual change. Insights are valuable in aiding curriculum developers in implementing effective ways to teach doctrinal principles by utilizing refutation text interventions. The advantages of this research study add to educational research and identify areas for improvement and exploration in further research. This study of refutation text effects in religious education also broadens researchers' understanding of refutation text's power for conceptual change in subjects outside of K-12 science. Results of this study are of interest to researchers, teachers, curriculum writers, and LDS seminary teachers and administrators.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.

Text categorization is the process of assigning new documents to predefined document categories on the basis of a classification model(s) induced from a set of pre-categorized training documents. In a typical dichotomous classification scenario, the set of training documents includes both positive and negative examples; that is, each of the two categories is associated with training documents. However, in many real-world text categorization applications, positive and unlabeled documents are readily available, whereas the acquisition of samples of negative documents is extremely expensive or even impossible. In this study, we propose and develop an ensemble approach, referred to as E2, to address the limitations of existing algorithms for learning from positive and unlabeled training documents. Using the spam email filtering as the evaluation application, our empirical evaluation results suggest that the proposed E2 technique exhibits more stable and reliable performance than PNB and PEBL.

While the amount of textual data available to us is constantly increasing, managing the texts by human effort is clearly inadequate for the volume and complexity of the information involved. Consequently, requirement for automated extraction of useful knowledge from huge amounts of textual data to assist human analysis is apparent. Text mining (TM) is mostly an automated technique that aims to discover knowledge from textual data. In this thesis, the notion of text mining, its techniques, applications are presented. In particular, the study provides the definition and overview of concepts in text categorization. This would include document representation models, weighting schemes, feature selection methods, feature extraction, performance measure and machine learning techniques. The thesis details the functionality of text mining as a quality improvement tool. It carries out an extensive survey of text mining applications within service sector and manufacturing industry. It presents two broad experimental studies tackling the potential use of text mining for the hotel industry (the comment card analysis), and in automobile manufacturer (miles per gallon analysis). Keywords: Text Mining, Text Categorization, Quality Improvement, Service Sector, Manufacturing Industry.

Recent advances in computer and network technologies have contributed significantly to global connectivity and stimulated the amount of online textual document to grow extremely rapidly. The rapid accumulation of textual documents on the Web or within an organization requires effective document management techniques, covering from information retrieval, information filtering and text mining. The word mismatch problem represents a challenging issue to be addressed by the document management research. Word mismatch has been extensively investigated in information retrieval (IR) research by the use of term expansion (or specifically query expansion). However, a review of text mining literature suggests that the word mismatch problem has seldom been addressed by text mining techniques. Thus, this thesis aims at investigating the use of term expansion on some text mining techniques, specifically including text categorization, document clustering and event detection. Accordingly, we developed term expansion extensions to these three text mining techniques. The empirical evaluation results showed that term expansion increased the categorization effectiveness when the correlation coefficient feature selection was employed. With respect to document clustering, techniques extended with term expansion achieved comparable clustering effectiveness to existing techniques and showed its superiority in improving clustering specificity measure. Finally, the use of term expansion for supporting event detection has degraded the detection effectiveness as compared to the traditional event detection technique.

<p>The purpose of this study is to present and analyze the translation of seven selected concessive conjuncts – anyway, however, although, though, still, nonetheless and yet – in Dan Brown’s novel Angels and Demons translated by Ola Klingberg, by means of a comparative method combined with a qualitative analysis. Background and theory are mainly based on Altenberg (1999, 2002) for the conjuncts and Ingo (1991) for translation strategies. The aim is fulfilled by answering the three research questions: 1. How does Klingberg translate the seven selected concessive conjuncts into Swedish? 2. What factors influence the choice of translation alternative? 3. What kinds of strategies does Klingberg use? The main result is that the conjuncts translate into many different alternatives, although most frequently into the Swedish adversative men, followed by a Swedish concessive like ändå. However, the analysis of anyway is inconclusive because there were not enough tokens. The main conclusion is that translation is a difficult area to be involved in since numerous aspects affect the choice of translation alternative, even though it is shown that it is definitely possible to translate more or less ‘correctly’. A second conclusion is that some words are more likely to be translated with a particular word than others.</p>

Einerseits werden bestehende Ergebnisse aus so unterschiedlichen Richtungen wie etwa der empirischen Medienforschung und dem Text Mining zusammengetragen. Es geht dabei um Inhaltsanalyse, von Hand, mit Unterstützung durch Computer, oder völlig automatisch, speziell auch im Hinblick auf die Faktoren wie Zeit, Entwicklung und Veränderung. Die Verdichtung und Zusammenstellung liefert nicht nur einen Überblick aus ungewohnter Perspektive, in diesem Prozess geschieht auch die Synthese von etwas Neuem. Die Grundthese bleibt dabei immer eine einschließende: So wenig es möglich scheint, dass in Zukunft der Computer Analysen völlig ohne menschliche Interpretation betreiben kann und wird, so wenig werden menschliche Interpretatoren noch ohne die jeweils bestmögliche Unterstützung des Rechners in der Lage sein, komplexe Themen zeitnah umfassend und ohne allzu große subjektive Einflüsse zu bearbeiten – und so wenig werden es sich substantiell wertvolle Analysen noch leisten können, völlig auf derartige Hilfen und Instrumente der Qualitätssicherung zu verzichten. Daraus ergeben sich unmittelbar Anforderungen: Es ist zu klären, wo die Stärken und Schwächen von menschlichen Analysten und von Computerverfahren liegen. Darauf aufbauend gilt es eine optimale Synthese aus beider Seiten Stärken und unter Minimierung der jeweiligen Schwächen zu erzielen. Praktisches Ziel ist letztlich die Reduktion von Komplexität und die Ermöglichung eines Ausgangs aus dem Zustand des systembedingten „overnewsed but uninformed“-Seins.