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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.

This study seeks to explore and develop innovative methods for the extraction of semantic knowledge from unlabelled written English documents and the representation of this knowledge using a formal mathematical expression to facilitate its use in practical applications. The first method developed in this research focuses on semantic information extraction. To perform this task, the study introduces a natural language processing (NLP) method designed to extract information-rich keywords from English sentences. The method involves initially learning a set of rules that guide the extraction of keywords from parts of sentences. Once this learning stage is completed, the method can be used to extract the keywords from complete sentences by pairing these sentences to the most similar sequence of rules. The key innovation in this method is the use of a part-of-speech hierarchy. By raising words to increasingly general grammatical categories in this hierarchy, the system can compare rules, compute the degree of similarity between them, and learn new rules. The second method developed in this study addresses the problem of knowledge representation. This method processes triplets of keywords through several successive steps to represent information contained in the triplets using possibility distributions. These distributions represent the possibility of a topic given a particular triplet of keywords. Using this methodology, the information contained in the natural language triplets can be quantified and represented in a mathematical format, which can be easily used in a number of applications, such as document classifiers. In further extensions to the research, a theoretical justification and mathematical development for both methods are provided, and examples are given to illustrate these notions. Sample applications are also developed based on these methods, and the experimental results generated through these implementations are expounded and thoroughly analyzed to confirm that the methods are reliable in practice.

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.

This study seeks to explore and develop innovative methods for the extraction of semantic knowledge from unlabelled written English documents and the representation of this knowledge using a formal mathematical expression to facilitate its use in practical applications. The first method developed in this research focuses on semantic information extraction. To perform this task, the study introduces a natural language processing (NLP) method designed to extract information-rich keywords from English sentences. The method involves initially learning a set of rules that guide the extraction of keywords from parts of sentences. Once this learning stage is completed, the method can be used to extract the keywords from complete sentences by pairing these sentences to the most similar sequence of rules. The key innovation in this method is the use of a part-of-speech hierarchy. By raising words to increasingly general grammatical categories in this hierarchy, the system can compare rules, compute the degree of similarity between them, and learn new rules. The second method developed in this study addresses the problem of knowledge representation. This method processes triplets of keywords through several successive steps to represent information contained in the triplets using possibility distributions. These distributions represent the possibility of a topic given a particular triplet of keywords. Using this methodology, the information contained in the natural language triplets can be quantified and represented in a mathematical format, which can be easily used in a number of applications, such as document classifiers. In further extensions to the research, a theoretical justification and mathematical development for both methods are provided, and examples are given to illustrate these notions. Sample applications are also developed based on these methods, and the experimental results generated through these implementations are expounded and thoroughly analyzed to confirm that the methods are reliable in practice.

No description available.