Abstract
Web search engines are useful tools which can meet the various information needs of users. However they often return hit-lists which contain many unnecessary pages. This paper proposes a method which automatically removes those unnecessary pages by learning filters through relevance feedback. Filters consist of several rules, each of which describes conditions for discriminating relevant pages using useful cooccurences or proximities among words, and parts in a page where those conditions are applicable. Thus they enable to classify a variety of pages precisely. Moreover, users are free to generate and apply filters at anytime. Through experiments we demonstarate that our filters increase the number of relevant pages we can get in a retrieval, compared to representatives of web search engine and relevance feedback method.
