Improved Relevancy Ranking Using Statistical Ranking, Semantics, Relevancy Feedback, and Small Pieces of Text
Search system and method for retrieving relevant documents from a text data base collection comprised of patents, medical and legal documents, journals, news stories and the like. Each small piece of text within the documents such as a sentence, phrase and semantic unit in the data base is treated as a document. Natural language queries are used to search for relevant documents from the data base. A first search query creates a selected group of documents. Each word in both the search query and in the documents are given weighted values. Combining the weighted values creates similarity values for each document which are then ranked according to their relevant importance to the search query. A user reading and passing through this ranked list checks off which documents are relevant or not. Then the system automatically causes the original search query to be updated into a second search query which can include the same words, less words or different words than the first search query. Wor
Application Serial Number
Assignee at Issuance
College of Engineering and Computer Science (CECS)
Assignee at Filing
Nonprovisional Application Record
Driscoll, James, "Improved Relevancy Ranking Using Statistical Ranking, Semantics, Relevancy Feedback, and Small Pieces of Text" (1997). UCF Patents. 719.