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Sentencing hearings are supposed to allow the jury to consider all evidence that's relevant to determining a sentence, including evidence of aggravating or mitigating factors.
And knowing how an animal died helps a judge determine a sentence.
Several existing sentence-level sentiment analysis methods, like VADER [15] and SO-CAL [31], combine a lexicon and the processing of the sentence characteristics to determine a sentence polarity.
In other words, we used VADER's code for determining if a sentence is positive or not considering different lexicons.
The liar sentence considered above leads to a contradiction when we try to determine whether it is true or not.
Determining that a sentence contains at least one interaction relation, i.e. it is an interaction sentence, may help to increase precision.
However, there are two different criteria for determining a match between a candidate sentence and the ground truth sentence: (i) exact match between sentence boundaries and (ii) partial overlapping.
To verify this hypothesis, we determine whether a sentence is an interaction sentence.
Each word in a sentence was considered in isolation, and a Support Vector Machine with a linear kernel, trained on human-annotated data, was used to determine if a given word was PHI.
For example, a word at end of a sentence is considered adjacent to the word in the beginning of the next sentence, and is treated the same way as when the two words were adjacent within the same sentence.
This was determined at a sentencing hearing last week before a nine-member military jury.
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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