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This type of features often contains grammatical or semantic meanings, and can represent words that do not appear in the labelled corpus effectively.
Objects belonging to the tool category (as compared to non-tools) are particularly likely to activate this type of features (e.g., Danckert and Goodale, 2001; Kenemans et al., 2000).
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Another approach is to use "region killer" software that bypasses the region coding, or a media player that includes this type of feature.
Secondly, it will be incredibly hard to adequately secure this type of feature, particularly given the ability to kill large numbers of phones in an area or even a whole country.
PTES is a methodology that provides this type of feature.
Of course, this type of feature quantization introduces the respective loss of the discriminative ability of the features.
This type of feature selection, collates quadratically transformed feature subsets for presentation to a collection of respective classifiers.
I hope in time Microsoft comes out with apps for the most popular platforms rather than keeping this type of feature phone-specific.
This shows the importance of this type of feature in training the machine-learning model and indicates that improving the quality of the dictionary could have an even greater impact on the final results.
While statistics point to a good number of users doing that even with the SUL, Twitter is unlikely to have gotten to where it is today without this type of feature.
Although this type of feature is most likely too far off for the Apple Watch 2.0, it's still something to consider moving forward as tech like the watch improves.
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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