ReadFast: Optimizing structural search relevance for big biomedical text

Abstract

While the problem to find needed information on the Web is critical, it is arguably much less pressing nowadays than it was over a decade ago when the Web was emerging. Back then it was much more difficult to find a Web resource of interest, because the search engines were in their infancy covering much lesser portion of the Web by their indices, armed with embryonic page ranking algorithms. Now, Web-search is by far not perfect yet, but definitely went a long way to become an everyday “go-to” resource for millions of people. By contrast, access to textual information is not even close to what Web-search algorithms offer today. In fact, it does not differ much from what everyone had a decade ago. That is keyword-search (exact substring match) is often the only way to find needle in a haystack in most modern word processors and text corpora search engines. Here we demonstrate ReadFast - a system, capable to extract certain structure from any natural language text corpus and use it to provide more relevant search results than keyword-search for specific classes of queries. Our evaluation justified significant relevance gain (20-30%) for two large Biomedical text corpora.