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In many cases keywords from a restricted set of possible keywords have to be assigned to texts. A common way to find the best keywords is to rank terms occurring in the text according to their tf.idf value. This requires a corpus of texts from which document frequencies can be derived. In this paper we show that we
Common search engines, especially web-based, rely on standard keyword-based queries and matching algorithms using word frequencies, topics recentness, documents authority and/or thesauri. However, even if those systems present efficient retrieval algorithms, they are not able to lead the user into an intuitive
builds another index, mapping the bug report keywords with developer bug fixing expertise. On arrival of new bug reports, the final module queries the two indexes using the new bug report terms, and applies tf-idf technique on the query result to calculate an ERBA score for developers. Finally, an ascending ordered list on
Web has grown to a huge mass of information resource and is diverse in content. To search such rich source of information one has to be very precise in using keywords in queries to retrieve the relevant documents. Most of the queries issued to search engines are short and have ambiguous context. One way to produce
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.