Title: 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.
Publication Year: 2013
Publication Date: 2013-08-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 4
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