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We use the Zettair search engine, using the Okapi BM25 similarity algorithm for ranking, to retrieve the 10,000 top ranked documents for each query made for the Drug set of 15 reviews.
are higher ranked documents and prevail over municipal or regional plans.
This could indicate that the classifier is able to accurately rank the documents, i.e. highly ranked documents indicate more desirable papers.
They collected the top ranked documents in retrieved sets to generate aspect queries.
We also report on performance of a possible stopping criterion for determining when to stop examining ranked documents.
In the process of executing the query containing the term A, ICE retrieves the top one thousand ranked documents.
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In addition, they applied rank fusion to combine different ranked document lists into a single list to improve the retrieval performance [1].
Then, those highly ranked document pairs were connected to each other in a network.
Among existing ranking algorithms, link analysis based algorithms have been proved to be effective for ranking documents retrieved from large-scale text repositories such as the current Web.
An information retrieval (IR) engine can rank documents based on textual proximity of keywords within each document.
Second, they give us a simple means for scoring (and thereby ranking) documents in response to a query.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com