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This effectiveness can be attributed largely to two factors: the use of contextual features by the machine learning system and the use of probabilistic evidence.
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In this article, the experience of China will be used to discuss the impact of contextual features on policies and practices of English language education.
To illustrate the importance of contextualized construct definition and content specification, I will use two cases to examine the alignment of contextual features with the aims and practices of English language education.
Cued upon recognition of contextual features, p-prims are used to construct intuitive understandings of the physical world.
Earlier in this article I have suggested that the paramount need in the description of contextual features is precise and orderly statements whose validity could be judged using theoretical principles.
It revolutionised the SFL view of language, with significant consequences for the realisation of contextual features.
This paper seeks to understand linguistic formality through the identification and measurement of contextual features.
He argues that the approach with parameters requires that a finite list of contextual features is given in an advance.
We measure the predictive power of a number of contextual features (e.g. social proximity, time, call/SMS).
We have shown quantitatively and qualitatively that recurrent combination of contextual features substantially enhances the initial estimates of local contrast.
Each cage group experienced each set of contextual features once.
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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