Automatic text summarization can efficiently and effectively save users¡¦ time while reading text documents. The objective of automatic text summarization is to extract essential sentences that cover almost all the concepts of a document so that users are able to comprehend the ideas the document tries to address by simply reading through the corresponding summary. This research focuses on developing a hybrid automatic text summarization approach, KCS, to enhancing the quality of summaries. This approach basically consists of two major components: first, it employs the K-mixture probabilistic model to calculate term weights in a statistical sense; it then identifies the term relationship between nouns and nouns as well as nouns and verbs, which results in the connective strength (CS) of nouns. With the connective strengths available scores of sentences can be calculated and ranked to be extracted. We conduct three experiments to justify the proposed approach. The quality of summary is examined by its capability of increasing accuracy of text classification,while the classifier employed, the Naïve Bayes classifier, is kept the same through all experiments. The results show that the K-mixture model is more contributive to document classification than traditional TFIDF weighting scheme. It, however, is still no better than CS, a more complex linguistic-based approach. More importantly, our proposed approach, KCS, performs best among all approaches considered. It implies that KCS can extract more representative sentences from the document and its feasibility in text summarization applications is thus justified.

Zugl.: Berlin, Humboldt-Univ., Diss., 2008

Thesis (Ph. D.)--University of Massachusetts Amherst, 2009. / Open access. Includes bibliographical references (p. 122-127). Print copy also available.

Denna studiesyftar till att undersöka typer av relationer mellan modaliteterna skrift och bild som tillsammans bildar en text. Centrala begrepp så som multimodalitet, literacy och det vidgade textbegreppet behandlasi teoridelen och återkopplas till i diskussionen. Metoden som valts är en innehållsanalys av text med Unsworths (2006) analysverktyg som utgångspunkt. Femläromedel har analyserats varav två storböcker, två lillböcker och en läsebok. I ett första skede har typer av relationer undersökts i de olika läromedlen och därefter har en jämförande analys gjorts för att undersöka eventuellaskillnader, i linjemedsyftet. Analysen visar på att typer av relationer mellan skrift och bild inte skiljer sig avsevärt åt mellan de utvalda läromedlen.Avvikelser är inte vanligtförekommande och en slutsats som kan dras av detta är att bilden inte endast finns till för att underhålla,utan tjänar ett syfte eftersom den överensstämmer med skriften. I deflesta fall är modaliteterna av samstämmighet och ökningav information. Ökningen skapar en möjlighet för ett utökat meningserbjudande avbudskapet som kan ge eleverna stöd i sin läsning.Ökningen kan också utnyttjas i samtalet runt innehållet i böckerna. Konjunktiva förbindelser i både textens skrift och bild kan också sägas vara en typ av ökning och kan hjälpa eleverna att orientera sig gällande tid, plats och händelse. En pedagogisk konsekvens av detta är atteleverna behöver få kunskap om att bilden och skriften kan användas som resurser för att förstå en text. Denna medvetenhet kan utveckla literacy hos eleverna.

Individuals, organizations, and devices are now interconnected to an unprecedented degree. This has forced identity risk analysts to redefine what “identity” means in such a context, and to explore new techniques for analyzing an ever expanding threat context. Major hurdles to modeling in this field include the inherent lack of publicly available data due to privacy and safety concerns, as well as the unstructured nature of incident reports. To address this, this report develops a system for strengthening an identity risk model using the text mining of news stories. The system—called NewsFerret—collects and analyzes news stories on the topic of identity theft, establishes semantic relatedness measures between identity concept pairs, and supports analysis of those measures through reports, visualizations, and relevant news stories. Evaluating the resulting analytical models shows where the system is effective in assisting the risk analyst to expand and validate identity risk models. / text

M. Tech. Information Networks / The study was undertaken to better understand the vast number of limitations in the use of mobile devices such as screen size, navigation, colour and network limitations. The aim of this study is to identify usability problems within the user interface design of mobile device instant messaging applications. The usability of three mobile instant messaging applications (MXit, Nimbuzz and Whatsapp) will be evaluated on three different high end touch screen smart phones (Apple iPhone 4, BlackBerry Torch 9800 and Nokia N8)

I mitten av 2000-talet gjorde Svenska dagbladet en granskning där tidningen kom fram till att läroböcker spred schablonbilder av invandrare. Det fanns flera undersökningar som visade att även om de negativa bilderna av invandring och invandrare hade tonats ner var böckerna inte klanderfria i sina skildringar. Eftersom jag anser att det är viktigt att läroböckerna inte sprider fördomar och negativa bilder av invandringen som kan leda till att den ökande främlingsfientligheten i vårt samhälla får mer näring, ville jag undersöka hur läroböckerna skildrade invandrare och invandring. I min undersökning valde jag att granska samhällskunskapsböcker på gymnasienivå. Jag valde att granska tre olika böcker och två upplagor av varje bok. En upplaga som kom före 2006 och en upplaga som kom efter 2006. Denna indelning i två perioder gjordes för att undersöka om läroboksförlag och läromedelsförfattare förändrade innehållet under den andra perioden eftersom både medier och forskning hade uppmärksammat problemet med skildringarna av invandringen och invandrare. Mina resultat stämmer överens med tidigare forskning. De visar att det inte förekommer rasistiska eller andra nedvärderande beskrivningar av invandrarna. Däremot knyts invandringen och invandrarna till problem och läroböckernas skildringar av invandringen och invandrare är sällan positiva. Min undersökning visar att de granskade läroböckernas skildringar av invandrare och invandring är något mer negativ under den andra undersökta perioden än den var under den första undersökningsperioden.

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There is a tremendous amount of information being generated and stored every year, and its growth rate is exponential. From 2008 to 2009, the growth rate was estimated to be 62%. In 2010, the amount of generated information is expected to grow by 50% to 1.2 Zettabytes, and by 2020 this rate is expected to grow to 35 Zettabytes. By preprocessing text in programmable logic, high data processing rates could be achieved with greater power efficiency than with an equivalent software solution, leading to a smaller carbon footprint. This thesis presents an overview of the fields of Information Retrieval and Natural Language Processing, and the design and implementation of four text preprocessing modules in programmable logic: UTF–8 decoding, stop–word filtering, and stemming with both Lovins’ and Porter’s techniques. These extensively pipelined circuits were implemented in a high performance FPGA and found to sustain maximum operational frequencies of 704 MHz, data throughputs in excess of 5 Gbps and efficiencies in the range of 4.332 – 6.765 mW/Gbps and 34.66 – 108.2 uW/MHz. These circuits can be incorporated into larger systems, such as document classifiers and information extraction engines.

Nowadays, academic researchers maintain a personal library of papers, which they would like to organize based on their needs, e.g., research, projects, or courseware. Clustering techniques are often employed to achieve this goal by grouping the document collection into different topics. Unsupervised clustering does not require any user effort but only produces one universal output with which users may not be satisfied. Therefore, document clustering needs user input for guidance to generate personalized clusters for different users. Semi-supervised clustering incorporates prior information and has the potential to produce customized clusters. Traditional semi-supervised clustering is based on user supervision in the form of labeled instances or pairwise instance constraints. However, alternative forms of user supervision exist such as labeling features. For document clustering, document supervision involves labeling documents while feature supervision involves labeling features. Their joint of use has been called dual supervision. In this thesis, we first explore and propose a framework to use feature supervision for interactive feature selection by indicating whether a feature is useful for clustering. Second, we enhance the semi-supervised clustering with feature supervision using feature reweighting. Third, we propose a unified framework to combine document supervision and feature supervision through seeding. The newly proposed algorithms are evaluated using oracles and demonstrated to be more helpful in producing better clusters matching a single user's point of view than document clustering without any supervision and with only document supervision. Finally, we conduct a user study to confirm that different users have different understandings of the same document collection and prefer personalized clusters. At the same time, we demonstrate that document clustering with dual supervision is able to produce good personalized clusters even with noisy user input. Dual supervision is also demonstrated to be more effective in personalized clustering than no supervision or any single supervision. We also analyze users' behaviors during the user study and present suggestions for the design of document management software.