Walker Percy writes, in the foreword of "A confederacy of dunces", “I hesitate to use the word comedy - though comedy it is - because that implies simply a funny book, and this novel is a great deal more than that …It is also sad. One never quite knows where the sadness comes from.” In this essay I have analyzed where the sadness comes from, through the psychoanalytic theories of Jaqcues Lacan, John Bowlby, Melanie Klein and Erich Fromm, mainly. My standpoint is that no text or utterance is ever completed, and therefore it is not absolute. The text itself is the strongest manifestation of power in the novel. When scrutinizing the text itself, I have treated it as one of the analysands, trying to reveal the suppressed information underneath the surface of the implied story.

Abbreviations are common in clinical documentation, as this type of text is written under time-pressure and serves mostly for internal communication. This study attempts to apply and extend existing rule-based algorithms that have been developed for English and Swedish abbreviation detection, in order to create an abbreviation detection algorithm for Swedish clinical texts that can identify and suggest definitions for abbreviations and acronyms. This can be used as a pre-processing step for further information extraction and text mining models, as well as for readability solutions. Through a literature review, a number of heuristics were defined for automatic abbreviation detection. These were used in the construction of the Swedish Clinical Abbreviation Normalizer (SCAN). The heuristics were: a) freely available external resources: a dictionary of general Swedish, a dictionary of medical terms and a dictionary of known Swedish medical abbreviations, b) maximum word lengths (from three to eight characters), and c) heuristics for handling common patterns such as hyphenation. For each token in the text, the algorithm checks whether it is a known word in one of the lexicons, and whether it fulfills the criteria for word length and the created heuristics. The final algorithm was evaluated on a set of 300 Swedish clinical notes from an emergency department at the Karolinska University Hospital, Stockholm. These notes were annotated for abbreviations, a total of 2,050 tokens. This set was annotated by a physician accustomed to reading and writing medical records. The algorithm was tested in different variants, where the word lists were modified, heuristics adapted to characteristics found in the texts, and different combinations of word lengths. The best performing version of the algorithm achieved an F-Measure score of 79%, with 76% recall and 81% precision, which is a considerable improvement over the baseline where each token was only matched against the word lists (51% F-measure, 87% recall, 36% precision). Not surprisingly, precision results are higher when the maximum word length is set to the lowest (three), and recall results higher when it is set to the highest (eight). Algorithms for rule-based systems, mainly developed for English, can be successfully adapted for abbreviation detection in Swedish medical records. System performance relies heavily on the quality of the external resources, as well as on the created heuristics. In order to improve results, part-of-speech information and/or local context is needed for disambiguation. In the case of Swedish, compounding also needs to be handled.

The wealth of medical-related information available today gives rise to a multidimensional source of knowledge. Research discoveries published in prestigious venues, electronic-health records data, discharge summaries, clinical notes, etc., all represent important medical information that can assist in the medical decision-making process. The challenge that comes with accessing and using such vast and diverse sources of data stands in the ability to distil and extract reliable and relevant information. Computer-based tools that use natural language processing and machine learning techniques have proven to help address such challenges. This current work proposes automatic reliable solutions for solving tasks that can help achieve a personalized-medicine, a medical practice that brings together general medical knowledge and case-specific medical information. Phenotypic medical observations, along with data coming from test results, are not enough when assessing and treating a medical case. Genetic, life-style, background and environmental data also need to be taken into account in the medical decision process. This thesis’s goal is to prove that natural language processing and machine learning techniques represent reliable solutions for solving important medical-related problems. From the numerous research problems that need to be answered when implementing personalized medicine, the scope of this thesis is restricted to four, as follows: 1. Automatic identification of obesity-related diseases by using only textual clinical data; 2. Automatic identification of relevant abstracts of published research to be used for building systematic reviews; 3. Automatic identification of gene functions based on textual data of published medical abstracts; 4. Automatic identification and classification of important medical relations between medical concepts in clinical and technical data. This thesis investigation on finding automatic solutions for achieving a personalized medicine through information identification and extraction focused on individual specific problems that can be later linked in a puzzle-building manner. A diverse representation technique that follows a divide-and-conquer methodological approach shows to be the most reliable solution for building automatic models that solve the above mentioned tasks. The methodologies that I propose are supported by in-depth research experiments and thorough discussions and conclusions.

We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization. The visualization encodes text data onto 3D models, emphasizing the relative signi ficance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models. We applied our method to analyze a collection of vehicle complaint reports from National Highway Traffic Safety Administration (NHTSA), and through a qualitative evaluation study, we demonstrate how our system can support tasks such as comparing the reliability of di fferent makes and models, finding interesting facts, and revealing possible causal relations between car parts. / UOIT

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With the advent research of Document Image Analysis and Recognition (DIAR), an important line of research is explored on indexing and retrieval of graphics rich docu- ments. It aims at nding relevant documents relying on segmentation and recognition of text and graphics components underlying in non-standard layout where commercial OCRs can not be applied due to complexity. This thesis is focused towards text infor- mation extraction approaches in graphical documents and retrieval of such documents using text information. Automatic text recognition in graphical documents (map, engineering drawing, etc.) involves many challenges because text characters are usually printed in multi- oriented and multi-scale way along with di erent graphical objects. Text characters are used to annotate the graphical curve lines and hence, many times they follow curvi-linear paths too. For OCR of such documents, individual text lines and their corresponding words/characters need to be extracted. For recognition of multi-font, multi-scale and multi-oriented characters, we have proposed a feature descriptor for character shape using angular information from con- tour pixels to take care of the invariance nature. To improve the e ciency of OCR, an approach towards the segmentation of multi-oriented touching strings into individual characters is also discussed. Convex hull based background information is used to segment a touching string into possible primitive segments and later these primitive segments are merged to get optimum segmentation using dynamic programming. To overcome the touching/overlapping problem of text with graphical lines, a character spotting approach using SIFT and skeleton information is included. Afterwards, we propose a novel method to extract individual curvi-linear text lines using the fore- ground and background information of the characters of the text and a water reservoir concept is used to utilize the background information. We have also formulated the methodologies for graphical document retrieval ap- plications using query words and seals. The retrieval approaches are performed using recognition results of individual components in the document. Given a query text, the system extracts positional knowledge from the query word and uses the same to generate hypothetical locations in the document. Indexing of documents is also per- formed based on automatic detection of seals from documents containing cluttered background. A seal is characterized by scale and rotation invariant spatial feature descriptors computed from labelled text characters and a concept based on the Generalized Hough Transform is used to locate the seal in documents. Keywords: Document Image Analysis, Graphics Recognition, Dynamic Pro- gramming, Generalized Hough Transform, Character Recognition, Touching Charac- ter Segmentation, Text/Graphics Separation, Curve-Line Separation, Word Retrieval, Seal Detection and Recognition.

Mitt arbete beskriver hur man kan arbeta ämnesövergripande med bild och text i kombination. Jag vill i mitt arbete genom exempel visa vikten av att bild och text vävs samman i undervisningen. Mitt val av arbete grundar sig på att mina elever saknade förståelse av bildspråket. Orden och texterna kom därför in som ett naturligt moment i undervisningen. Syftet med arbetet är att undersöka hur text och bild i kombination kan ge en djupare förståelse för bildspråket. Ett ämnesövergripande arbete genomfördes. Frågeställningar: Är det betydelsefullt för eleverna att använda text, ord och bild i kombination? Hur kan man arbeta ämnesövergripande med text, ord och bild? I resultatet av denna studie pekar på behov av större förståelse av att med ord och text i kombination arbeta ämnesövergripande inom bildundervisningen. Senare i diskussionen tas betydelsen av att text och bild i kombination upp. Betydelsen av att vi lärare bygger upp ett arbetsklimat av samverkan, vilket ger eleverna en starkare helhet mellan de olika ämnesområdena poängteras. I resultatet av mitt arbete visar att eleverna har ett behov att nå en större förståelse för hur man arbetar text, ord och bild i kombination. Samarbetet ger eleverna en större helhet. Senare i diskussionen tar jag upp vikten av att text, ord och bild hänger ihop. Mina slutsatser grundar jag på resultatet av min studie.

The purpose of this study was to, from the teacher's point of view; discover pupils with reading difficulties in connection with text assignments in mathematics, the teacher's support for these students, if difficulties coincide and whether it's the mathematics teacher's responsibility to make sure pupils with reading difficulties develop their reading ability. The study is based on a qualitative method and five semistructured interviews with mathematics teachers and one language support teacher. The result shows there is a strong connection between the pupils' reading ability and their ability to manage text assignments in mathematics. A weak reading ability affects the pupil in a negative way when solving text assignments in mathematics due to wrong reading technique if the teacher is absent. It is not unusual that reading difficulties and mathematics difficulties coincide. The teacher's most important role in the learning process is to teach the pupils understanding about the reading technique in different ways and to give the pupils opportunity to get spoken texts. It is not the mathematics teacher's responsibility to make sure pupils with reading difficulties develop their reading ability, rather it's the responsibility of a specialist and professional reading- and writing support teacher.

A <i>web crawler</i> is a program that "walks" the Web to gather web resources. In order to scale to the ever-increasing Web, multiple crawling agents may be deployed in a distributed fashion to retrieve web data co-operatively. A common approach is to divide the Web into many partitions with an agent assigned to crawl within each one. If an agent obtains a web resource that is not from its partition, the resource will be transferred to the rightful owner. This thesis proposes a novel approach to distributed web data gathering by partitioning the Web into topics. The proposed approach employs multiple focused crawlers to retrieve pages from various topics. When a crawler retrieves a page of another topic, it transfers the page to the appropriate crawler. This approach is known as <i>topic-oriented collaborative web crawling</i>. An implementation of the system was built and experimentally evaluated. In order to identify the topic of a web page, a topic classifier was incorporated into the crawling system. As the classifier categorizes only English pages, a language identifier was also introduced to distinguish English pages from non-English ones. From the experimental results, we found that redundance retrieval was low and that a resource, retrieved by an agent, is six times more likely to be retained than a system that uses conventional hashing approach. These numbers were viewed as strong indications that <i>topic-oriented collaborative web crawling system</i> is a viable approach to web data gathering.

I vår studie kommer vi att jämföra och analysera elevers språksocialisation i olika skolor.  Undersökningen handlar om hur elever i olika socioekonomiska områden använder sig av språket i olika situationer samt stödet och uppmuntran de får i sitt första språk. Vårt syfte är att jämföra och se om det finns skillnader mellan olika skolor och områden, tanken är att se hur deras flerspråkighet uttrycker sig i klassrummet eller andra situationer när de använder sig av språket. För att nå vårt syfte har vi valt att intervjua flerspråkiga elever på två olika skolar som ligger i två olika socioekonomiska områden. Vi har valt att intervjua 10 elever och valt fem elever från vardera skola.  Det vi tolkat utifrån de resultat vi har fått är att det finns vissa skillnader mellan elevernas syn på deras flerspråkighet i de olika områdena. Skillnaderna är att flerspråkighetens betydelse i områdena är olika då de på ena skolan ser att de kan använda sina kunskaper i många olika situationer i jämförelse med den andra skolan som vi har tolkat anser att deras flerspråkighet är begränsad. En annan skillnad är att på skola ett är det mer normalt att använda sig av sin flerspråkighet i skolsituationer i jämförelse med skola två där de endast används under modersmålsundervisningen.