The project shall evaluate and extend the state-of-the-art in document clustering according to semantics extracted from natural language documents. This will require testing of current methods to identify their limitations and proposal of new methods based on empirical observations. Two complementary techniques shall be evaluated: methods for incremental taxonomy growth and for calculating semantic similarity among documents.
Search engines, such as Google, have revolutionized the way we search for electronic information, providing a user with a ranked list of documents most relevant for a particular query. This project with GenieKnows R&D, a search engine company, concerns an extension of this basic technology, in which the goal is to incorporate geographic constraints into the search (e.g. find coffee shops near the Halifax Citadel). An additional challenge is to learn and incorporate user preferences when ranking the results returned.
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