We first learn a ranking function over the entire extraction collection using a limited set of textual features, including weighted sentences, proximities, and expansion terms. This function is then used to retrieve the best possible subset of documents Mexico Phone Number List on which the final model is trained using a larger set of query and document dependent features. » (vang et al, 2013) while the 2013 document is older, that's one more reason Mexico Phone Number List why progress will have improved, as the two-step system is still "The industry standard." second stage of the two-stage classification: the reclassification from this list of retrieved documents, a second pass is performed over a specified number of top-x documents.
Known as the top-k of the retrieved document list and refined for accuracy using d machine learning. You will often see in information research papers the term (accuracy to k) which refers to levels of accuracy in the top k relative to a relevant "Gold standard" or "Ground truth" (k being a number, 10 would mean the number of accurate Mexico Phone Number List results judged to meet the user's information needs with respect to a query among the first 10 results retrieved). A good explanation of valuation metrics such as pk (and there are a number of others) is provided in this information research lecture slide.
The second stage of the two-stage ranking is where accuracy is much more important and much more resources are spent, while possibly adding other relevance metrics to really separate the gold in the top ranks. The importance of more accurately Mexico Phone Number List Mexico Phone Number List ranking documents selected for inclusion in stage 2 is critical, and the accuracy of highly ranked results, especially as the likelihood of these results being seen by search engine users is high. . As the saying goes, “only seos look beyond the second page of search results.” in “learning to classify in two steps for information retrieval.