3 research outputs found
Grammatical Functions and Possibilistic Reasoning for the Extraction and Representation of Semantic Knowledge in Text Documents
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
Language-independent pre-processing of large document bases for text classification
Text classification is a well-known topic in the research of knowledge discovery in
databases. Algorithms for text classification generally involve two stages. The first
is concerned with identification of textual features (i.e. words andlor phrases) that
may be relevant to the classification process. The second is concerned with
classification rule mining and categorisation of "unseen" textual data. The first
stage is the subject of this thesis and often involves an analysis of text that is both
language-specific (and possibly domain-specific), and that may also be
computationally costly especially when dealing with large datasets. Existing
approaches to this stage are not, therefore, generally applicable to all languages. In
this thesis, we examine a number of alternative keyword selection methods and
phrase generation strategies, coupled with two potential significant word list
construction mechanisms and two final significant word selection mechanisms, to
identify such words andlor phrases in a given textual dataset that are expected to
serve to distinguish between classes, by simple, language-independent statistical
properties. We present experimental results, using common (large) textual datasets
presented in two distinct languages, to show that the proposed approaches can
produce good performance with respect to both classification accuracy and
processing efficiency. In other words, the study presented in this thesis
demonstrates the possibility of efficiently solving the traditional text classification
problem in a language-independent (also domain-independent) manner