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And the R-Token Standard ostensibly provides an interface that embeds compliance at the token level and can be implemented in a way that ensures that specified requirements like investor caps and holding periods are met before a trade is approved.
Performance of the recognizer at the token level, include precision values ranging from 90.06% (TARGET) to 98.10% (EXPO), while recall values range from 85.50% (EXPO) to 94.30% (NANO).
However, this annotation would lead to an increase in recall for the NANO category at the token level, since the system successfully recognized 2 tokens (out of 3) in the phrase " metallic nickel nanoparticles" as belonging to the NANO category.
The same type of operations is allowed at the token level, so that the system is able to handle minor string variations, as for instance between diarrhea and diarrhoea.
Following the literature, the evaluations on the first two processes were assessed at the token level, while the evaluation on medication matching was assessed at the span level to ensure that each medication and its attributes were evaluated only once [ 37, 39].
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Experiment I has P, R, and F-measure of 0.919, 0.731, and 0.814, respectively, on the token level; and 0.886, 0.766, and 0.822, respectively, at the exact span match level.
It might ensure reductionism at the token-level, but it might, at the same time, be committed to dualism at the property level though perhaps only a relatively innocent form of dualism.
Thus, the mention "titanium dioxide particles", belonging to the NANO category at entity-level would be counted as 3 different mentions of the NANO category at the token-level.
For each word, the token-level properties (e.g. capitalization and punctuation), the context (e.g. tokens before and after the studied token) and the part-of-speech tags were then used as text features in the CRF model.
We made this distinction to evaluate the performance of the system both at entity-level (exact matching) and at token level (partial or inexact matching).
The table shows the precision, recall, and F-measure for each target category (NANO, EXPO, TOXIC, and TARGET) both at entity and token level.
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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