Thesis (M.S.)--Missouri University of Science and Technology, 2010. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 19, 2010) Includes bibliographical references (p. 72-75).

Text clustering divides a set of texts into groups, so that texts within each group are similar in content. It may be used to uncover the structure and content of unknown text sets as well as to give new perspectives on known ones. The contributions of this thesis are an investigation of text representation for Swedish and an evaluation method that uses two or more manual categorizations. Text clustering, at least such as it is treated here, is performed using the vector space model, which is commonly used in information retrieval. This model represents texts by the words that appear in them and considers texts similar in content if they share many words. Languages differ in what is considered a word. We have investigated the impact of some of the characteristics of Swedish on text clustering. Since Swedish has more morphological variation than for instance English we have used a stemmer to strip suffixes. This gives moderate improvements and reduces the number of words in the representation. Swedish has a rich production of solid compounds. Most of the constituents of these are used on their own as words and in several different compounds. In fact, Swedish solid compounds often correspond to phrases or open compounds in other languages.In the ordinary vector space model the constituents of compounds are not accounted for when calculating the similarity between texts. To use them we have employed a spell checking program to split compounds. The results clearly show that this is beneficial. The vector space model does not regard word order. We have tried to extend it with nominal phrases in different ways. Noneof our experiments have shown any improvement over using the ordinary model. Evaluation of text clustering results is very hard. What is a good partition of a text set is inherently subjective. Automatic evaluation methods are either intrinsic or extrinsic. Internal quality measures use the representation in some manner. Therefore they are not suitable for comparisons of different representations. External quality measures compare a clustering with a (manual) categorization of the same text set. The theoretical best possible value for a measure is known, but it is not obvious what a good value is -- text sets differ in difficulty to cluster and categorizations are more or less adapted to a particular text set. We describe an evaluation method for cases where a text set has more than one categorization. In such cases the result of a clustering can be compared with the result for one of the categorizations, which we assume is a good partition. We also describe the kappa coefficient as a clustering quality measure in the same setting. / Textklustring delar upp en mängd texter i grupper, så att texterna inom dessa liknar varandra till innehåll. Man kan använda textklustring för att uppdaga strukturer och innehåll i okända textmängder och för att få nya perspektiv på redan kända. Bidragen i denna avhandling är en undersökning av textrepresentationer för svenska texter och en utvärderingsmetod som använder sig av två eller fler manuella kategoriseringar. Textklustring, åtminstonde som det beskrivs här, utnyttjar sig av den vektorrumsmodell, som används allmänt inom området. I denna modell representeras texter med orden som förekommer i dem och texter som har många gemensamma ord betraktas som lika till innehåll. Vad som betraktas som ett ord skiljer sig mellan språk. Vi har undersökt inverkan av några av svenskans egenskaper på textklustring. Eftersom svenska har större morfologisk variation än till exempel engelska har vi tagit bort suffix med hjälp av en stemmer. Detta ger lite bättre resultat och minskar antalet ord i representationen. I svenska används och skapas hela tiden fasta sammansättningar. De flesta delar av sammansättningar används som ord på egen hand och i många olika sammansättningar. Fasta sammansättningar i svenska språket motsvarar ofta fraser och öppna sammansättningar i andra språk. Delarna i sammansättningar används inte vid likhetsberäkningen i vektorrumsmodellen. För att utnyttja dem har vi använt ett rättstavningsprogram för att dela upp sammansättningar. Resultaten visar tydligt att detta är fördelaktigt I vektorrumsmodellen tas ingen hänsyn till ordens inbördes ordning. Vi har försökt utvidga modellen med nominalfraser på olika sätt. Inga av våra experiment visar på någon förbättring jämfört med den vanliga enkla modellen. Det är mycket svårt att utvärdera textklustringsresultat. Det ligger i sakens natur att vad som är en bra uppdelning av en mängd texter är subjektivt. Automatiska utvärderingsmetoder är antingen interna eller externa. Interna kvalitetsmått utnyttjar representationen på något sätt. Därför är de inte lämpliga att använda vid jämförelser av olika representationer. Externa kvalitetsmått jämför en klustring med en (manuell) kategorisering av samma mängd texter. Det teoretiska bästa värdet för måtten är kända, men vad som är ett bra värde är inte uppenbart -- mängder av texter skiljer sig åt i svårighet att klustra och kategoriseringar är mer eller mindre lämpliga för en speciell mängd texter. Vi beskriver en utvärderingsmetod som kan användas då en mängd texter har mer än en kategorisering. I sådana fall kan resultatet för en klustring jämföras med resultatet för en av kategoriseringarna, som vi antar är en bra uppdelning. Vi beskriver också kappakoefficienten som ett kvalitetsmått för klustring under samma förutsättningar. / QC 20101220

We all have access to large collections of digital text documents, which are useful only if we can make sense of them all and distill important information from them. Good document clustering algorithms that organize such information automatically in meaningful ways can make a difference in how effective we are at using that information. In this paper we use model-based document clustering algorithms as a base for bisecting methods in order to identify increasingly cohesive clusters from larger, more diverse clusters. We specifically use the EM algorithm and Gibbs Sampling on a mixture of multinomials as the base clustering algorithms on three data sets. Additionally, we apply a refinement step, using EM, to the final output of each clustering technique. Our results show improved agreement with human annotated document classes when compared to the existing base clustering algorithms, with marked improvement in two out of three data sets.

Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets.

A necessidade de extrair conhecimento útil e inovador de grandes massas de dados textuais, tem motivado cada vez mais a investigação de métodos para Mineração de Textos. Dentre os métodos existentes, destacam-se as iniciativas para organização de conhecimento por meio de hierarquias de tópicos, nas quais o conhecimento implícito nos textos é representado em tópicos e subtópicos, e cada tópico contém documentos relacionados a um mesmo tema. As hierarquias de tópicos desempenham um papel importante na recupera ção de informação, principalmente em tarefas de busca exploratória, pois permitem a análise do conhecimento de interesse em diversos níveis de granularidade e exploração interativa de grandes coleções de documentos. Para apoiar a construção de hierarquias de tópicos, métodos de agrupamento hierárquico têm sido utilizados, uma vez que organizam coleções textuais em grupos e subgrupos, de forma não supervisionada, por meio das similaridades entre os documentos. No entanto, a maioria dos métodos de agrupamento hierárquico não é adequada em cenários que envolvem coleções textuais dinâmicas, pois são exigidas frequentes atualizações dos agrupamentos. Métodos de agrupamento que respeitam os requisitos existentes em cenários dinâmicos devem processar novos documentos assim que são adicionados na coleção, realizando o agrupamento de forma incremental. Assim, neste trabalho é explorado o uso de métodos de agrupamento incremental para o aprendizado não supervisionado de hierarquias de tópicos em coleções textuais dinâmicas. O agrupamento incremental é aplicado na construção e atualização de uma representação condensada dos textos, que mantém um sumário das principais características dos dados. Os algoritmos de agrupamento hierárquico podem, então, ser aplicados sobre as representa ções condensadas, obtendo-se a organização da coleção textual de forma mais eficiente. Foram avaliadas experimentalmente três estratégias de agrupamento incremental da literatura, e proposta uma estratégia alternativa mais apropriada para hierarquias de tópicos. Os resultados indicaram que as hierarquias de tópicos construídas com uso de agrupamento incremental possuem qualidade próxima às hierarquias de tópicos construídas por métodos não incrementais, com significativa redução do custo computacional / The need to extract new and useful knowledge from large textual collections has motivated researchs on Text Mining methods. Among the existing methods, initiatives for the knowledge organization by topic hierarchies are very popular. In the topic hierarchies, the knowledge is represented by topics and subtopics, and each topic contains documents of similar content. They play an important role in information retrieval, especially in exploratory search tasks, allowing the analysis of knowledge in various levels of granularity and interactive exploration of large document collections. Hierarchical clustering methods have been used to support the construction of topic hierarchies. These methods organize textual collections in clusters and subclusters, in an unsupervised manner, using similarities among documents. However, most existing hierarchical clustering methods is not suitable for scenarios with dynamic text collections, since frequent clustering updates are necessary. Clustering methods that meet these requirements must process new documents that are inserted into textual colections, in general, through incremental clustering. Thus, we studied the incremental clustering methods for unsupervised learning of topic hierarchies for dynamic text collections. The incremental clustering is used to build and update a condensed representation of texts, which maintains a summary of the main features of the data. The hierarchical clustering algorithms are applied in these condensed representations, obtaining the textual organization more efficiently. We experimentally evaluate three incremental clustering algorithms available in the literature. Also, we propose an alternative strategy more appropriate for construction of topic hieararchies. The results indicated that the topic hierarchies construction using incremental clustering have quality similar to non-incremental methods. Furthermore, the computational cost is considerably reduced using incremental clustering methods

A necessidade de extrair conhecimento útil e inovador de grandes massas de dados textuais, tem motivado cada vez mais a investigação de métodos para Mineração de Textos. Dentre os métodos existentes, destacam-se as iniciativas para organização de conhecimento por meio de hierarquias de tópicos, nas quais o conhecimento implícito nos textos é representado em tópicos e subtópicos, e cada tópico contém documentos relacionados a um mesmo tema. As hierarquias de tópicos desempenham um papel importante na recupera ção de informação, principalmente em tarefas de busca exploratória, pois permitem a análise do conhecimento de interesse em diversos níveis de granularidade e exploração interativa de grandes coleções de documentos. Para apoiar a construção de hierarquias de tópicos, métodos de agrupamento hierárquico têm sido utilizados, uma vez que organizam coleções textuais em grupos e subgrupos, de forma não supervisionada, por meio das similaridades entre os documentos. No entanto, a maioria dos métodos de agrupamento hierárquico não é adequada em cenários que envolvem coleções textuais dinâmicas, pois são exigidas frequentes atualizações dos agrupamentos. Métodos de agrupamento que respeitam os requisitos existentes em cenários dinâmicos devem processar novos documentos assim que são adicionados na coleção, realizando o agrupamento de forma incremental. Assim, neste trabalho é explorado o uso de métodos de agrupamento incremental para o aprendizado não supervisionado de hierarquias de tópicos em coleções textuais dinâmicas. O agrupamento incremental é aplicado na construção e atualização de uma representação condensada dos textos, que mantém um sumário das principais características dos dados. Os algoritmos de agrupamento hierárquico podem, então, ser aplicados sobre as representa ções condensadas, obtendo-se a organização da coleção textual de forma mais eficiente. Foram avaliadas experimentalmente três estratégias de agrupamento incremental da literatura, e proposta uma estratégia alternativa mais apropriada para hierarquias de tópicos. Os resultados indicaram que as hierarquias de tópicos construídas com uso de agrupamento incremental possuem qualidade próxima às hierarquias de tópicos construídas por métodos não incrementais, com significativa redução do custo computacional / The need to extract new and useful knowledge from large textual collections has motivated researchs on Text Mining methods. Among the existing methods, initiatives for the knowledge organization by topic hierarchies are very popular. In the topic hierarchies, the knowledge is represented by topics and subtopics, and each topic contains documents of similar content. They play an important role in information retrieval, especially in exploratory search tasks, allowing the analysis of knowledge in various levels of granularity and interactive exploration of large document collections. Hierarchical clustering methods have been used to support the construction of topic hierarchies. These methods organize textual collections in clusters and subclusters, in an unsupervised manner, using similarities among documents. However, most existing hierarchical clustering methods is not suitable for scenarios with dynamic text collections, since frequent clustering updates are necessary. Clustering methods that meet these requirements must process new documents that are inserted into textual colections, in general, through incremental clustering. Thus, we studied the incremental clustering methods for unsupervised learning of topic hierarchies for dynamic text collections. The incremental clustering is used to build and update a condensed representation of texts, which maintains a summary of the main features of the data. The hierarchical clustering algorithms are applied in these condensed representations, obtaining the textual organization more efficiently. We experimentally evaluate three incremental clustering algorithms available in the literature. Also, we propose an alternative strategy more appropriate for construction of topic hieararchies. The results indicated that the topic hierarchies construction using incremental clustering have quality similar to non-incremental methods. Furthermore, the computational cost is considerably reduced using incremental clustering methods

The explosive growth in the number of documents produced daily necessitates the development of effective alternatives to explore, analyze, and discover knowledge from documents. Document mining research work has emerged to devise automated means to discover and analyze useful information from documents. This work has been mainly concerned with constructing text representation models, developing distance measures to estimate similarities between documents, and utilizing that in mining processes such as document clustering, document classification, information retrieval, information filtering, and information extraction. <br /><br /> Conventional text representation methodologies consider documents as bags of words and ignore the meanings and ideas their authors want to convey. It is this deficiency that causes similarity measures to fail to perceive contextual similarity of text passages due to the variation of the words the passages contain, or at least perceive contextually dissimilar text passages as being similar because of the resemblance of words the passages have. <br /><br /> This thesis presents a new paradigm for mining documents by exploiting semantic information of their texts. A formal semantic representation of linguistic inputs is introduced and utilized to build a semantic representation scheme for documents. The representation scheme is constructed through accumulation of syntactic and semantic analysis outputs. A new distance measure is developed to determine the similarities between contents of documents. The measure is based on inexact matching of attributed trees. It involves the computation of all distinct similarity common sub-trees, and can be computed efficiently. It is believed that the proposed representation scheme along with the proposed similarity measure will enable more effective document mining processes. <br /><br /> The proposed techniques to mine documents were implemented as vital components in a mining system. A case study of semantic document clustering is presented to demonstrate the working and the efficacy of the framework. Experimental work is reported, and its results are presented and analyzed.

The explosive growth in the number of documents produced daily necessitates the development of effective alternatives to explore, analyze, and discover knowledge from documents. Document mining research work has emerged to devise automated means to discover and analyze useful information from documents. This work has been mainly concerned with constructing text representation models, developing distance measures to estimate similarities between documents, and utilizing that in mining processes such as document clustering, document classification, information retrieval, information filtering, and information extraction. <br /><br /> Conventional text representation methodologies consider documents as bags of words and ignore the meanings and ideas their authors want to convey. It is this deficiency that causes similarity measures to fail to perceive contextual similarity of text passages due to the variation of the words the passages contain, or at least perceive contextually dissimilar text passages as being similar because of the resemblance of words the passages have. <br /><br /> This thesis presents a new paradigm for mining documents by exploiting semantic information of their texts. A formal semantic representation of linguistic inputs is introduced and utilized to build a semantic representation scheme for documents. The representation scheme is constructed through accumulation of syntactic and semantic analysis outputs. A new distance measure is developed to determine the similarities between contents of documents. The measure is based on inexact matching of attributed trees. It involves the computation of all distinct similarity common sub-trees, and can be computed efficiently. It is believed that the proposed representation scheme along with the proposed similarity measure will enable more effective document mining processes. <br /><br /> The proposed techniques to mine documents were implemented as vital components in a mining system. A case study of semantic document clustering is presented to demonstrate the working and the efficacy of the framework. Experimental work is reported, and its results are presented and analyzed.

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.

Recent advances in information and networking technologies have contributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns in this study) in sequences of documents. The discovery of such evolution patterns can be applied in such domains as knowledge management and used to facilitate existing document management and retrieval techniques (e.g., event tracking). Specifically, we propose and design an evolution pattern (EP) discovery technique for mining evolution patterns from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating event tracking. Measured by miss and false alarm rates, the evolution-pattern supported event-tracking (EPET) technique exhibits better tracking effectiveness than a traditional event-tracking technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of evolution patterns in supporting event tracking and suggests that the proposed EP technique could effectively discover event episodes and evolution patterns in sequences of documents.