| This thesis targets the problems of language variability (i.e. polysemy and synonymy) from the viewpoint of lexical semantic similarity—a measure of semantic/conceptual similarity between pairs of lexicalized concepts represented in words or terms. As is often the case for many tasks in information retrieval (IR) and natural language processing (NLP), a job is decomposed to the requirement of resolving the semantic relation between lower-level constituents such as words or concepts. One needs to develop a consistent, widely applicable computational model to assess this type of relation.; We believe that a proper identification of similarity between concepts would contribute significantly in resolving semantic ambiguity in general. We start by looking at the fundamentals of the concept of similarity, its assumptions and characteristics. A new framework of universal object comparison and similarity determination scheme is then constructed in set-theoretic notions. This is in response to the observation that there is generally a lack of systematic classification and definition of various similarity formulae. Typically, a similarity formula or metric is directly employed in a problem without much theoretical justification and the assumptions behind it are not stated explicitly. In our study, rather than directly stipulate a similarity definition, we intend to derive it from a set of reasonable and intuitively justifiable assumptions. We then argue that this framework provides a general account for modeling object comparisons, and some of the specific comparison schemes can be further abstracted and quantified using information-theoretic notions so that a simple computational means of measuring universal object similarity can be achieved.; To realize such a computational object comparison scheme, we propose a new model of measuring lexical semantic similarity given the context of a lexical taxonomy. This model enhances the graph distance approach by properly quantifying the weight of each edge along the shortest path that links two concept nodes in the taxonomic hierarchy.; This core similarity model is then applied to several levels of applications in NLP and IR. (Abstract shortened by UMI.)... |
