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Theater directors take liberties on a regular basis with canonical texts, understanding that to recapture the energy and impact of a treasured work in a new form, reinvention is a necessity, not an indulgence.
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This article describes KERNEL, a text understanding system developed at Paramax Systems Corporation.
The text understanding of Wolfram Alpha has advanced steadily, with hundreds of subject domains added.
This paper describes a parser in progress which is directed to generating representations for text understanding.
For short text understanding, we experimented with the task of classifying news titles into categories.
Without text understanding, the only hint of their actual POS is the presence of capital letters.
A particularity of this approach is that learning occurs simultaneously with the on-going text understanding process.
This abundant probability information among concepts and instances allows us to provide additional context in text understanding.
Our proposed character-based textual embeddings allow the replacement of both word-embeddings and recurrent neural networks for text understanding, saving processing time and requiring fewer learnable parameters.
Grobelnik [14] states the importance of an integration of these research areas in order to reach a complete solution to the problem of text understanding.
This area of machine learning, with its strange name, is starting to quietly infiltrate different applications ranging from text understanding to processing and comprehending what's in a photo.
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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