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Because this text is publicly available, it is an ideal corpus for testing future thresholding algorithms against the algorithm suggested in this article.
We opted for two different methods to train and test these systems: 1) Using the i2b2 de-identification challenge corpus[ 19] for training, and our 225-document corpus for testing.
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All research is based on Polish speech corpus ROBOT designed for testing speech algorithms.
In the sequel, the EPAC corpus is used only for testing.
All research is based on Polish speech corpus ROBOT, which is designed for testing speech algorithms.
Also a manually annotated corpus of structured and unstructured abstracts is prepared for testing a classifier that identifies sentences belonging to each category.
Beigman Klebanov and Beigman, 2009 suggest researchers should report the level of noise in a corpus and only use non-noisy (easy) instances for testing.
To keep the test corpus valid for testing, only the training/development sets were used to identify issues and performance improvements were validated against these sets.
The SI84 part of Wall Street Journal corpus was used for training the acoustic models and the November 1992 ARPA CSR test set was used for testing.
For testing the language model and vocabulary, we randomly chose 1,500 testing utterances from the Albayzin corpus.
Learning curves for accuracy, sensitivity, and specificity constructed by gradually increasing the training corpus for a test corpus fixed to 50 abstracts (25 relevant and irrelevant, respectively) supports this notion.
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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