LE3 .A278 2009

2009

Chipman, Hugh
Ranjan, Pritam

Acadia University

Master of Science

Masters

Statistics

Mathematics & Statistics

Query classification is an important and yet challenging problem for the search engine industry and e-commerce companies. In this thesis, I develop a query classification system based on a novel query expansion approach and classification methods. The proposed methodology is used to classify queries based on a taxonomy (a database of words and their corresponding topic classification). The taxonomy used was obtained from GenieKnows, a vertical search engine company in Halifax, Canada. The query classification system can be divided into three phases: feature selection, query expansion, and query classification. The rst phase uses a chi-square statistic to select a subset of \feature words" from the GenieKnows taxonomy; the second phase uses cosine similarity and Kullback-Leibler divergence to nd \feature words" similar to the query for query expansion; and nally the third phase introduces three classication methods: naive Bayes multinomial model, naive Bayes Bernoulli model and Dirichlet/multinomial model to classify the expanded queries. Results from the KDD-Cup 2005 competition are used to test the performance of the proposed query classification system. The experiment shows that the performance of the query classification system is quite good.

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https://scholar.acadiau.ca/islandora/object/theses:138