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Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.
Canonicalization of filenames is important for computer security. For example, a web server may have a restriction that only files under the cgi directory C:\inetpub\wwwroot\cgi-bin may be executed. This rule is enforced by checking that the path starts with C:\inetpub\wwwroot\cgi-bin\ and only then executing it.
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.
For example, each record in a "Faculty and Their Courses" relation might contain a Faculty ID, Faculty Name, Faculty Hire Date, and Course Code. Therefore, the details of any faculty member who teaches at least one course can be recorded, but a newly hired faculty member who has not yet been assigned to teach any courses cannot be recorded ...
The following normalizations are described in RFC 3986 [1] to result in equivalent URIs: . Converting percent-encoded triplets to uppercase. The hexadecimal digits within a percent-encoding triplet of the URI (e.g., %3a versus %3A) are case-insensitive and therefore should be normalized to use uppercase letters for the digits A-F. [2] Example:
Kathy Ireland took a major gamble in finding her husband.. While on the MovieGuide Awards red carpet, Ireland explained that she "stalked" her husband, Dr. Greg Olsen, before officially meeting.
Python's runtime does not restrict access to such attributes, the mangling only prevents name collisions if a derived class defines an attribute with the same name. On encountering name mangled attributes, Python transforms these names by prepending a single underscore and the name of the enclosing class, for example:
Columns of any conceivable data type (from string types and numeric types to array types and table types) are then acceptable in a 1NF table—although perhaps not always desirable; for example, it may be more desirable to separate a Customer Name column into two separate columns as First Name, Surname.