Document retrieval systems recover documents from a database and order them according to their perceived relevance to a user's search query. This is a difficult task for machines to accomplish because there exists a semantic gap between the meaning of the terms in a user's literal query and a user's true intentions. The main goal of this study is to modify the Okapi BM25 document retrieval system to improve search results for textual queries and unstructured, textual corpora. This research hypothesizes that Okapi BM25 is not taking full advantage of the structure of text inside documents. This structure holds valuable semantic information that can be used to increase the model's accuracy. Modifications that account for a term's part of speech, the proximity between a pair of related terms, the proximity of a term with respect to its location in a document, and query expansion are used to augment Okapi BM25. The study resulted in 87 modifications which were all validated using open source corpora. The top scoring modification from the validation set was then tested under the Lisa corpus and the model performed 10.25% better than Okapi BM25 when evaluated under mean average precision.


Computer Sciences

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