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The example sentence has unambiguous meaning to anatomists, but each word in the sentence can have many different meanings.
Look at our example sentence.
Her example sentence (ex.
The following sentences are example sentences from Medline abstracts that describe drug interactions within the individual sentences.
The data set did not contain any negative example sentences.
Finally the pipeline outputs the protein names and GO categories along with the counts of how often they appear together within the sentence or non-sentence spans.
Table 4 lists example sentences.
The second dataset (BC-PPI) consisted of 173 sentences with at least one annotated relation and 743 negative example sentences.
Following this, we need to extract the most relevant words in each of the sentences (in the example given above it would be "brilliant," "not" and "working") and rank them based on their frequency of appearance within the data.
For example, the sentence He ate the carambola licenses the inference that carambola refers to something edible.
In the case of the example corpus (PubMed abstracts), all of the sentences from the abstract are squeezed into a single string of words, obliterating sentence boundaries.
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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