CCE Theses and Dissertations
Campus Access Only
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Date of Award
2009
Document Type
Dissertation - NSU Access Only
Degree Name
Doctor of Philosophy in Computer Information Systems (DCIS)
Department
Graduate School of Computer and Information Sciences
Advisor
Sumitra Mukherjee
Committee Member
Amon B Seagull
Committee Member
Francisco Mitropoulos
Keywords
automatic thesaurus construction, fuzzy rough sets, inverse term frequency, synonyms
Abstract
One of the problems associated with automatic thesaurus construction is with determining the semantic relationship between word pairs. Quasi-synonyms provide a type of equivalence relationship: words are similar only for purposes of information retrieval. Determining such relationships in a thesaurus is hard to achieve automatically. The term vector space model and an inverse term frequency similarity function can provide a way to automatically determine the similarity between words in thesaurus. A thesaurus constructed using this method can also improve precision and recall in information retrieval, when the thesaurus is constructed in conjunction with fuzzy rough set algorithms and used with tight upper approximation query expansion. This dissertation presents a method that combines fuzzy rough sets and a word weighting and inverse term frequency similarity function as a technique for automatic thesaurus construction.
NSUWorks Citation
Julius Mack Davault III. 2009. Resolving Quasi-Synonym Relationships in Automatic Thesaurus Construction using Fuzzy Rough Sets and an Inverse Term Frequency Similarity Function. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Graduate School of Computer and Information Sciences. (129)
https://nsuworks.nova.edu/gscis_etd/129.