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Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text ...
Dimensional normalization, or snowflaking, removal of redundant attributes in a dimensional model; NFD normalization (normalization form canonical decomposition), a normalization form decomposition for Unicode string searches and comparisons in text processing; Spatial normalization, a step in image processing for neuroimaging
In computer science, canonicalization (sometimes standardization or normalization) is a process for converting data that has more than one possible representation into a "standard", "normal", or canonical form.
Instance normalization (InstanceNorm), or contrast normalization, is a technique first developed for neural style transfer, and is also only used for CNNs. [26] It can be understood as the LayerNorm for CNN applied once per channel, or equivalently, as group normalization where each group consists of a single channel:
Canonical form can also mean a ... the reduction of data to any kind of canonical form is commonly called data normalization. ... Text is available under ...
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines, logistic regression, and artificial neural networks).
“Text messages that are intended to be positive, are interpreted as more neutral than they're intended to be,” Greenberg notes. “And neutral messages are interpreted as more negative than ...
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.