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.

The last decade has been marked by unprecedented growth in both the production of biomedical data and the amount of published literature discussing it. Almost every known or postulated piece of information pertaining to genes, proteins, and their role in biological processes is reported somewhere in the vast amount of published biomedical literature. We believe the ability to rapidly survey and analyze this literature and extract pertinent information constitutes a necessary step toward both the design and the interpretation of any large-scale experiment. Moreover, automated literature mining offers a yet untapped opportunity to integrate many fragments of information gathered by researchers from multiple fields of expertise into a complete picture exposing the interrelated roles of various genes, proteins, and chemical reactions in cells and organisms. In this thesis, we show that functional keywords in biomedical literature, particularly Medline, represent very valuable information and can be used to discover new genomic knowledge. To validate our claim we present an investigation into text mining biomedical literature to assist microarray data analysis, yeast gene function classification, and biomedical literature categorization. We conduct following studies: 1. We test sets of genes to discover common functional keywords among them and use these keywords to cluster them into groups; 2. We show that it is possible to link genes to diseases by an expert human interpretation of the functional keywords for the genes- none of these diseases are as yet mentioned in public databases; 3. By clustering genes based on commonality of functional keywords it is possible to group genes into meaningful clusters that reveal more information about their functions, link to diseases and roles in metabolism pathways; 4. Using extracted functional keywords, we are able to demonstrate that for yeast genes, we can make a better functional grouping of genes in comparison to available public microarray and phylogenetic databases; 5. We show an application of our approach to literature classification. Using functional keywords as features, we are able to extract epidemiological abstracts automatically from Medline with higher sensitivity and accuracy than a human expert.

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.

Effective knowledge management of proliferating volume of documents within a knowledge repository is vital to knowledge sharing, reuse, and assimilation. In order to facilitate accesses to documents in a knowledge repository, use of a knowledge map to organize these documents represents a prevailing approach. Document clustering techniques typically are employed to produce knowledge maps. However, existing document clustering techniques are not tailored to individuals¡¦ preferences and therefore are unable to facilitate the generation of knowledge maps from various preferential perspectives. In response, we propose the Preference-Anchored Document Clustering (PAC) technique that takes a user¡¦s categorization preference (represented as a list of anchoring terms) into consideration to generate a knowledge map (or a set of document clusters) from this specific preferential perspective. Our empirical evaluation results show that our proposed technique outperforms the traditional content-based document clustering technique in the high cluster precision area. Furthermore, benchmarked with Oracle Categorizer, our proposed technique also achieves better clustering effectiveness in the high cluster precision area. Overall, our evaluation results demonstrate the feasibility and potential superiority of the proposed PAC technique.

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.

Document clustering automatically organizes a document collection into distinct groups of similar documents on the basis of their contents. Most of existing document clustering techniques deal with monolingual documents (i.e., documents written in one language). However, with the trend of globalization and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for multilingual document clustering (MLDC). Motivated by its significance and need, this study designs a Latent Semantic Indexing (LSI) based MLDC technique. Our empirical evaluation results show that the proposed LSI-based multilingual document clustering technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision.

With the advances in information and networking technologies and the proliferation of e-commerce and knowledge management applications, individuals and organizations generate and acquire tremendous amount of online information that is typically available as textual documents. To manage the ever-increasing volume of documents, an individual or organization frequently organizes his/her documents into a set or hierarchy of categories in order to facilitate document management and subsequent information access and browsing. Furthermore, document clustering is an intentional act that reflects individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document-clustering must consider individual preferences for supporting personalization in document categorization and should be capable of organizing documents into a category hierarchy. However, document-clustering research traditionally has been anchored in analyses of document content. As a consequence, most of existing document-clustering techniques are not tailored to individuals¡¦ preferences and therefore are unable to facilitate personalization. On the other hand, existing document-clustering techniques generally are designed to generate from a document collection a set of document clusters rather than a hierarchy of document clusters. In response, we develop in this study a hierarchical personalized document-clustering (HPEC) technique that takes into account an individual¡¦s folder hierarchy representing the individual¡¦s categorization preferences and produces document-clusters in a hierarchical structure for the target individual. Our empirical evaluation results suggest that the proposed HPEC technique outperformed its benchmark technique (i.e., HAC+P) in cluster recall while maintaining the same level of cluster precision and location discrepancy as its benchmark technique did.

With the emergence of the Internet, many innovative and interesting applications from different countries have been stimulated and e-commerce is also getting more and more pervasive. Under this scenario, tremendous amount of information expressed in different languages are exchanged and shared by not only organizations but also individuals in the modern global environment. A large proportion of information is typically formatted and available as textual documents and managed by using categories. Consequently, the development of a practical and effective technique to deal with the problem of cross-lingual category integration (CLCI) becomes a very essential and important issue. Several category integration techniques have been proposed, but all of them deal with category integration involving only monolingual documents. In response, in this study, we combine the existing cross-lingual text categorization techniques with an existing monolingual category integration technique (specifically, Enhanced Naive Bayes) and proposed a CLCI solution to address cross-lingual category integration. Our empirical evaluation results show that our proposed CLCI technique demonstrates its feasibility and superior effectiveness.

According to the context theory of classification, the document-clustering behaviors of individuals not only involve the attributes (including contents) of documents but also depend on who is doing the task and in what context. Thus, effective document-clustering techniques need to be able to take into account users¡¦ categorization preferences and thus can generate document clusters from different preferential perspectives. The Preference-Anchored Document Clustering (PAC) technique was proposed for supporting preference-based document-clustering. Specifically, PAC takes a user¡¦s categorization preference into consideration and subsequently generates a set of document clusters from this specific preferential perspective. In this study, we attempt to investigate two research questions concerning the PAC technique. The first research question investigates ¡§whether the incorporation of the broader-term expansion (i.e., the proposed PAC2 technique in this study) will improve the effectiveness of preference-based document-clustering, whereas the second research question is ¡§whether the use of a statistical-based thesaurus constructed from a larger document corpus will improve the effectiveness of preference-based document-clustering.¡¨ Compared with the effectiveness achieved by PAC, our empirical results show that the proposed PAC2 technique neither improves nor deteriorates the effectiveness of preference-based document-clustering when the complete set of anchoring terms is used. However, when only a partial set of anchoring terms is provided, PAC2 cannot improve and even deteriorate the effectiveness of preference-based document-clustering. As to the second research question, our empirical results suggest the use of a statistical-based thesaurus constructed from a larger document corpus (i.e., the ACM corpus consisting of 14,729 documents) does not improve the effectiveness of PAC and PAC2 for preference-based document-clustering.

To manage the ever-increasing volume of documents, organizations and individuals typically organize documents into categories (or category hierarchies) to facilitate their document management and support subsequent document retrieval and access. Document clustering is an intentional act that should reflect individuals¡¦ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user¡¦s categorization context defined by or relevant to the target task under consideration. However, existing document clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate personalized or context-aware document clustering. In response, we design, implement and empirically evaluate three document clustering techniques capable of facilitating personalized or contextual document clustering. First, we extend an existing document clustering technique (specifically, the partial-clustering-based personalized document-clustering (PEC) approach) and propose the Collaborative Filtering¡Vbased personalized document-Clustering (CFC) technique to overcome the problem of small-sized partial clustering encountered by the PEC technique. Particularly, the CFC technique expands the size of a user¡¦s partial clustering based on the partial clusterings of other users with similar categorization preferences. Second, to support contextual document clustering, we design and implement a Context-Aware document-Clustering (CAC) technique by taking into consideration a user¡¦s categorization preference (i.e., a set of anchoring terms) relevant to the context of a target task and a statistical-based thesaurus constructed from the World Wide Web (WWW) via a search engine. Third, in response to the problem of small-sized set of anchoring terms which can greatly degrade the effectiveness of the CAC technique, we extend CAC and propose a Collaborative Filtering-based Context-Aware document Clustering (CF-CAC) technique. Our empirical evaluation results suggest that our proposed CFC, CAC, and CF-CAC techniques better support the need of personalized and contextual document clustering than do their benchmark techniques.