Vast amount of radiology reports are produced in hospitals. Being in free text format and having errors due to rapid production, it continuously gets more complicated for radiologists and physicians to reach meaningful information. Though application of ontologies into bio-medical text mining has gained increasing interest in recent years, less work has been offered for ontology based retrieval tasks in Turkish language. In this work, an information extraction and retrieval system based on SNOMED-CT ontology has been proposed for Turkish radiology reports. Main purpose of this work is to utilize semantic relations in ontology to improve precision and recall rates of search results in domain. Practical problems encountered such as spelling errors, segmentation and tokenization of unstructured medical reports has also been addressed during the work.

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.

No description available.

Due to the ever-increasing volume of textual documents, text mining is a rapidly growing application of knowledge discovery in databases. Past text mining techniques predominately concentrated on discovering intra-document patterns from textual documents, such as text categorization, document clustering, query expansion, and event tracking. Mining inter-document patterns from textual documents has been largely ignored in the literature. This research focuses on discovering inter-document patterns, called evolution patterns, from document-sequences and proposed the evolution pattern discovery (EPD) technique for mining evolution patterns from a set of ordered sequences of documents. The discovery of evolution patterns can be applied in such domains as environmental scanning and knowledge management, and can be used to facilitate existing document management and retrieval techniques (e.g., event tracking).

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>In this masters’ thesis report we present a graph theoretical method used for automatic bilingual lexicon acquisition with parallel texts. We analyze the concept of graph similarity and give an interpretation, of the parallel texts, connected to the vector space model. We represent the parallel texts by a directed, tripartite graph and from here use the corresponding adjacency matrix, A, to compute the similarity of the graph. By solving the eigenvalue problem ρS = ASAT + ATSA we obtain the self-similarity matrix S and the Perron root ρ. A rank k approximation of the self-similarity matrix is computed by implementations of the singular value decomposition and the non-negative matrix factorization algorithm GD-CLS. We construct an algorithm in order to extract the bilingual lexicon from the self-similarity matrix and apply a statistical model to estimate the precision, the correctness, of the translations in the bilingual lexicon. The best result is achieved with an application of the vector space model with a precision of about 80 %. This is a good result and can be compared with the precision of about 60 % found in the literature.</p>

In der Diplomarbeit wird die Weiterentwicklung eines Prototyps zur Nachrichtenklassifikation sowie die Integration in das bestehende Web-orientierte Informationssystem (WEBIS) beschrieben. Mit der entstandenen Software vorgenommene Klassifikationen werden vorgestellt und mit bisherigen Erkenntnissen verglichen.

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.

Univ., Diss--Bern, 2005.

Text mining has become an established discipline both in research as in business intelligence. However, many existing text mining toolkits lack easy extensibility and provide only poor support for interacting with statistical computing environments. Therefore we propose a text mining framework for the statistical computing environment R which provides intelligent methods for corpora handling, meta data management, preprocessing, operations on documents, and data export. We present how well established text mining techniques can be applied in our framework and show how common text mining tasks can be performed utilizing our infrastructure. The second part in this thesis is dedicated to a set of realistic applications using our framework. The first application deals with the implementation of a sophisticated mailing list analysis, whereas the second example identifies the potential of text mining methods for business to consumer electronic commerce. The third application shows the benefits of text mining for law documents. Finally we present an application which deals with authorship attribution on the famous Wizard of Oz book series. (author's abstract)