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The trained text embeddings are then fed into logistic regression classifier [5] for classification.
To this end, the trained text embedding is able to predict important words in larger probabilities while ignore those unimportant ones.
The authors showed that this technique improved text reading when the trained and probe texts shared a significant number of words, but that there was no generalization to probe texts that had no words in common with the trained text.
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It is a reasonable result since PV-GloVe and PV essentially utilize text, word co-occurrence to train text embeddings.
Because CaRE was trained on text from discharge summaries rather than doctor-patient conversations, we also believe that its performance can be improved significantly once we train it on appropriate corpora (which we do not yet have).
Tables and supplementary material are not of the same type, and a sequence classifier trained on text would not be expected to work well on data with such different characteristics.
The accuracy is so good that you no longer have to begin by reading a four-minute training text, as in years past.
The line obviously isn't in Apple's training text as frequently as "Four score and seven years ago .Another much-touted advance in iOS8 is predictive text for typing.
Tellingly, some portions of Facebook's training text copy Wikipedia's own language verbatim.
First of all the sparse text is split into two, a training text corpus and a development text corpus.
Two models (RNNLM-Freq/Brown) with 300 hidden units are trained on the entire training text for comparisons.
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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