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Moreover, entropy is a useful concept accounting for quantifying the information in a textual sequence and making a connection with probabilistic language modelling.
The algorithm summarizes protein 3D-structure in a textual sequence and applies a cross-entropy concept over n-gram modelling in order to capture similarities between protein sequences.
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In this study, the linear-chain CRF model was employed to recognize sequentially the boundaries of section headings for a given tokenized textual sequence (CRF++, which is available at http://taku910.github.io/crfpp/, was used to implement the linear-chain CRF model).
Textual sequence similarity is taken as a surrogate for common ancestry and, by extension, functional and structural similarity.
Once associated with words in a textual document, these labels constitute relevant features of a text.
Interactions are initially established in a network by entity co-occurrences in a textual collection.
Almost all CPGs are provided in a textual format, sometimes including a few algorithms and tables.
Provide this in a textual format or a graphic chart.
If you're doing most of the talking in a textual exchange, it's time to stop texting.
Disseminating CPGs in such a textual format has proved inefficient.
The LIBRAS translator is responsible for translating the source input stream (i.e., the closed caption stream) into a textual representation in LIBRAS (sequence of glosses) and for encoding this representation along with synchronization information to be transmitted in a communication channel.
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