Sentence examples for for a given document from inspiring English sources

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.

For a given document, the user can select from among the appropriate analysis tools to process the data; for example, in the case of a sequence document, the user may choose to perform a BLAST (Altschul et al., 1990) search for a given query sequence against a specific online repository, align against other sequences or generate protein translations.

Each entity mention in the list had to be unique (for a given document).

In addition, a keypage-based search mechanism is used to find related papers for protein protein interactions from a given document.

➢ Chemical Entity Mention (CEM) recognition: The required output of this subtask is the start and end character index pairs for the chemical entities mentioned in a given document.

For example, if a user visited a given document N times, and that document contains a concept X and a tag T, then both X and T are incorporated to the user profile with a frequency of N.

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.

Please note, though, that you may submit no more than 1,000 words of any given document for a quick evaluation and "triage" (i.e., only up to 1000 words for a whole thesis).

Furthermore, the "cleanxml" annotator now extracts the reference date for a given XML document, so relative dates, e.g., "yesterday", are transparently normalized with no configuration necessary.

Different XML schema may use different methods to indicate where in the document section breaks and even text content occur, while it cannot be guaranteed that well-formed inline annotations can be generated for a given input document.

In most cases, in addition to the identification rate, the Top-K identification rates are also reported where for a given query document, a list of most similar K writers is retrieved which increases the chances of finding the true writer of the query document.