Children’s understanding of race constancy and their subsequent use of race as a means of drawing inductive inferences were investigated. Race constancy was determined by children’s tendency to say that people could change category membership by changing their outside appearance. A second phase of the study measured how many race-based inferences children made relative to other social categories such as age or sex. The results indicated that children who had a better understanding of race constancy were also more likely to use race as a means of drawing inductive inferences. These findings support a developmental progression of race constancy and give insight to the development of potential bias and stereotypes.

The aim of this study is to examine how ADHD is discussed in the Swedish mass media. We have examined 21 articles from Aftonbladet, Expressen, Dagens Nyheter, Göteborgs-Posten and Svenska Dagbladet. All articles are reviewed from the newspapers online edition. Theories used are Erving Goffman’s theory of Stigma and Michael Foucault´s theory of categorization as well as Göran Palm's, Renée Skogersson's and Anders R. Olsson's theories about mass media. We have also used relevant literature. The main method we used is of Norman Fairclough´s three-dimensional critical discourse analysis where we in most parts have focused on the text analysis and also with the help of text coding system. One of our conclusions is that ADHD is sometimes described with negative words such as "brat" or "problem child". With these words we discovered the existence of underlying messages of the articles that are not expressed clearly. ADHD-medications were often described negative in the articles and also the reasons why a person might actually have ADHD were found to be discussed in the mass media. / Syftet med detta examensarbete är att studera hur ADHD framställs i svensk massmedia. 21 artiklar från Aftonbladet, Expressen, Dagens Nyheter, Göteborgs-Posten och Svenska Dagbladet har analyserats. Vi har hämtat artiklarna från tidningarnas internet- upplaga.  Vi har använt oss av Norman Fairclough’s tredimensionella kritiska diskursanalys och har fokuserat på textanalysen. För att granska artiklarna har vi genomfört en textkodning för att bland annat söka efter olika nyckelord i artiklarna. För att skapa en djupare förståelse har vi använt Erving Goffman’s teori om Stigma och Michael Foucault’s teori om Kategorisering samt Göran Palm's, Renée Skogersson's och Anders R. Olsson's teorier om massmedia. Vi har även använt oss av relevant litteratur i analysen. En av våra iakttagelser är att personer med ADHD ibland beskrivs med ord som till exempel ”satunge” eller ”problembarn”. Genom att vara uppmärksam på dessa ord upptäckte vi att det fanns underliggande budskap i artiklarna som inte uttrycktes explicit. Orsakerna till ADHD, ökningen av diagnoserna och medicinering skrivs det mycket om i de valda artiklarna. Dessa tre ”teman” hänger intimt ihop. Orsakerna påverkar ökningen som i sin tur påverkar medicineringen. ADHD- medicineringen framställs ofta negativt i vårt material, bland annat belyser vissa artiklar att det saknas forskning om långtidseffekterna av medicineringen. Även orsakerna till varför en person egentligen får ADHD visar sig vara omdiskuterat i de valda artiklarna.

The information overload problem becomes imperative with the explosion of information, and people need some agents to facilitate them to filter the information to meet their personal need. In this work, we conduct a research for the article classification in the tourism domain so as to identify articles that meet users¡¦ information need. We propose an information need orientation model in tourism, which consists of four goals: Initiation, Attraction, Accommodation, and Route planning. These goals can be characterized by 13 features. Some of the identified features can be enhanced by WordNet and Named Entity Recognition techniques as supplement techniques. To test the effectiveness of using the 13 features for classification and the relevant methods, we collected 15,797 articles from TripAdvisor.com, the world's largest travel site, and randomly selected 600 articles as training data labeled by two labelers. The experimental results show that our approach generally has comparable or better performance than that of using purely lexical features, namely TF-IDF, for classification, with fewer features.

Literature digital library is one of the most important resources to preserve civilized asset. To provide more effective and efficient information search, many systems are equipped with a browsing interface that aims to ease the article searching task. A browsing interface is associated with a subject directory, which guides the users to identify articles that need their information need. A subject directory contains a set (or a hierarchy) of subject categories, each containing a number of similar articles. How to group articles in a literature digital library is the theme of this thesis. Previous work used either document classification or document clustering approaches to dispatching articles into a set of article clusters based on their content. We observed that articles that meet a single user¡¦s information need may not necessarily fall in a single cluster. In this thesis, we propose to make use of both Web log and article content is clustering articles. We proposed two hybrid approaches, namely document categorization based method and document clustering based method. These alternatives were compared to other content-based methods. It has been found that the document categorization based method effectively reduces the number of required click-through at the expense of slight increase of entropy that measures the content heterogeneity of each generated cluster.

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.

In this thesis, we make automatic video categorization and summarization by using subtitles of videos. We propose two methods for video categorization. The first method makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The method starts with text preprocessing. Then a keyword extraction algorithm and a word sense disambiguation method are applied. The WordNet domains that correspond to the correct senses of keywords are extracted. Video is assigned a category label based on the extracted domains. The second method has the same steps for extracting WordNet domains of video but makes categorization by using a learning module. Experiments with documentary videos give promising results in discovering the correct categories of videos. Video summarization algorithms present condensed versions of a full length video by identifying the most significant parts of the video. We propose a video summarization method using the subtitles of videos and text summarization techniques. We identify significant sentences in the subtitles of a video by using text summarization techniques and then we compose a video summary by finding the video parts corresponding to these summary sentences.

This thesis, studies automated methods to achieve web service advertisement and discovery, and presents efficient search and matching techniques based on OWL-S. In the proposed system, the service discovery and matchmaking is performed via a centralized peer-to-peer web service repository. The repository has the ability to run on a software cloud, which improves the availability and scalability of the service discovery. The service advertisement is done semi-automatically on the client side, with an automatic WSDL to OWL-S conversion, and manual service description annotation. An OWL-S based unified ontology -Suggested Upper Merged Ontology- is used during annotation, to enhance semantic matching abilities of the system. The service advertisement and availability are continuously monitored on the client side to improve the accuracy of the query results. User-agents generate query specification using the system ontology, to provide semantic unification between the client and the system during service discovery. Query matching is performed via complex Hilbert Spaces composed of conceptual planes and categorical similarities for each web service. User preferences following the service queries are monitored and used to improve the service match scores in the long run.

In this dissertation, we present a framework to enhance the retrieval, ranking, and categorization of text documents in medical domain. The contributions of this study are the introduction of a similarity model to retrieve and rank medical textdocuments and the introduction of rule-based categorization method based on lexical syntactic patterns features. We formulate the similarity model by combining three features to model the relationship among document and construct a document network. We aim to rank retrieved documents according to their topics / making highly relevant document on the top of the hit-list. We have applied this model on OHSUMED collection (TREC-9) in order to demonstrate the performance effectiveness in terms of topical ranking, recall, and precision metrics. In addition, we introduce ROLEX-SP (Rules Of LEXical Syntactic Patterns) / a method for the automatic induction of rule-based text-classifiers relies on lexical syntactic patterns as a set of features to categorize text-documents. The proposed method is dedicated to solve the problem of multi-class classification and feature imbalance problems in domain specific text documents. Furthermore, our proposed method is able to categorize documents according to a predefined set of characteristics such as: user-specific, domain-specific, and query-based categorization which facilitates browsing documents in search-engines and increase users ability to choose among relevant documents. To demonstrate the applicability of ROLEX-SP, we have performed experiments on OHSUMED (categorization collection). The results indicate that ROLEX-SP outperforms state-of-the-art methods in categorizing short-text medical documents.

Researchers are still far from thoroughly understanding and building accurate computational models of the mechanisms in human mind that give rise to cognitive processes such as emergence of concepts and language acquisition. As a new attempt to give an insight into this issue, in this thesis, we are concerned about developing a computational model that leads to the emergence of concepts. Specically, we investigate how a robot can acquire verb and object concepts through learning affordances, a notion first proposed by J. J. Gibson in 1986. Using the affordance formalization framework of Sahin et al. in 2007, a humanoid robot acquires concepts through interactions in an embodied environment. For the acquisition of verb concepts, we take an alternative approach to the literature, which generally links verbs to specific behaviors of the robot, by linking them to specific effects that different behaviors may generate. We show how our robot can learn effect prototypes, represented in terms of feature changes in the perception vector of the robot, through demonstrations made by a human supervisor. As for the object concepts, we use the affordance relations of objects to create object concepts based on their functional relevance. Additionally, we show that the extracted eect prototypes corresponding to verb concepts can also be utilized to discover stable and variable properties of objects which can be associated to stable and variable affordances. Moreover, we show that the acquired concepts provide a suitable basis for communication with humans or other agents, for example to understand and imitate others&#039 / behaviors or for goal specication tasks. These capabilities are demonstrated in simple interaction games on the iCub humanoid robot platform.