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If we look at the language model perplexity, we have achieved significant reduction about 11% relatively in comparison with the reference model trained on unclassified text corpora, if we applied combination of tf-idf weighting with similarity based on Jaccard correlation index in the text classification process.
We refer to ln(Avg/Min) value as 'spike index' in the text.
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We called this ratio "Mmp-Index" in the text.
Instead, Eppright et al. [15] demonstrated significantly lower plaque index scores in the text message group just at T2, which was four appointments or an average of 5.44 months after baseline (T0).
Damage Index is defined in the text above (figure adapted from Maqsood et al., 2014, p93).
On the other hand, we show how to employ this idea in conjunction with an index over the text, allowing to search a pattern, with up to k mismatches, in time proportional to its length.
Searching allows data to be identified using these filter criteria in addition to an index of the text extracted from relevant file types.
Depending on the field, you may need to index every author named in the text.
For example, OneRiot, based in Boulder, Colo., covers Twitter among other social media, but it has an intriguing means of reducing Twitter spam: it does not index the text in tweets — it plucks only the links, reasoning that the videos, news stories and blog posts that are being shared are what others will be most interested in.
Anticipation indices were estimated using two methods – referred to in the text as Stoleru/Relative Index [5] and Harrisingh/Individual Index [51].
The plaque index was 0.65 (Iqr, 0.60) in the text message group and 0.34 (Iqr, 0.23) in the telephone call group (Wilcoxon rank-sum, P = 0.5980).
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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