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In this method, the standard Penn Treebank tokenization rules [15] are utilized to tokenize the given document.
First, in the pre-processing stage, a rule-based method is employed to tokenize the given document, as will be elaborated in the following subsection.
Users upload their PDFs to Yahoo's ad network and then Yahoo hosts the document for download and serves up contextual advertising in a panel to the right of the given document's content.
In our approach, we used machine learning to identify document keywords, which would likely be used frequently in user queries and become click-words for the given document.
Another difference is that along with each predicted GO term for the given gene in the given document, output of associated evidence text is also required in the earlier task.
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In this approach, the researcher built a template which is text snippets to represent the given documents and generate the summary.
CEM subtask is the task to provide for a given document the start and end indices corresponding to all the chemical entities mentioned in the document.
Researchers following this method illustrated the content of given document using a dependency tree.
Since they were in a hurry, the shredders tended to stuff all the bits of a given document into a single bag.
Content classification automates tagging by comparing the content of a given document against a pre-set list of classification categories.
Since many words do not appear in any given document, the matrix is often sparse.
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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