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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 ...
Unicode equivalence is the specification by the Unicode character encoding standard that some sequences of code points represent essentially the same character. This feature was introduced in the standard to allow compatibility with pre-existing standard character sets, which often included similar or identical characters.
A Canonical XML document is by definition an XML document that is in XML Canonical form, defined by The Canonical XML specification. Briefly, canonicalization removes whitespace within tags, uses particular character encodings, sorts namespace references and eliminates redundant ones, removes XML and DOCTYPE declarations, and transforms ...
Normalization model, used in visual neuroscience; Normalization in quantum mechanics, see Wave function § Normalization condition and normalized solution; Normalization (sociology) or social normalization, the process through which ideas and behaviors that may fall outside of social norms come to be regarded as "normal"
In HTML and XML, a numeric character reference refers to a character by its Universal Character Set/Unicode code point, and uses the format: &#xhhhh;. or &#nnnn; where the x must be lowercase in XML documents, hhhh is the code point in hexadecimal form, and nnnn is the code point in decimal form.
For codes from 0 to 127, the original 7-bit ASCII standard set, most of these characters can be used without a character reference. Codes from 160 to 255 can all be created using character entity names. Only a few higher-numbered codes can be created using entity names, but all can be created by decimal number character reference.
Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity .