Abstract

This paper describes an implementation of query-by-example, or relevance feedback, for text. The implementation uses Google's search engine to perform a keyword query as requested by the user. If the user requires more information, the user may score documents in the result set as relevant or irrelevant. An implementation of the AdaBoost algorithm is then used choose words that separate the relevant documents from a random document set. Examples of negative document sets are also tested. An example query and refinements of the query is presented. The results seem promising. The system seems to propose new keywords that are sensible to the requested context. Many of the keywords prove useful in constructing new queries. However, refinement using exactly the new terms predicted by the system does not seem to return noticeably better or worse results. This may be the result of an inexact fit between the design of AdaBoost and the capabilities of Google as a back end engine. ...

Citations