Artificial Intelligence - هوش مصنوعی  
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بازگشت   Artificial Intelligence - هوش مصنوعی > داده کاوی > پردازش و کاوش متن( Text Mining and processing )


 
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Iranian Association for the Advancement of Artificial Intelligence
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قديمي ۰۶-۲۹-۱۳۹۱, ۰۳:۲۲ بعد از ظهر   #1 (لینک دائم)
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پيش فرض Tokenization

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قديمي ۰۷-۴-۱۳۹۱, ۰۹:۰۶ قبل از ظهر   #2 (لینک دائم)
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اگر كسي كمكم كنه ممنون ميشم

Tokenization is the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens. The list of tokens becomes input for further processing such as parsing or text mining. Tokenization is useful both in linguistics (where it is a form of text segmentation), and in computer science, where it forms part of lexical analysis.
[edit]Methods and obstacles

Typically, tokenization occurs at the word level. However, it is sometimes difficult to define what is meant by a "word". Often a tokenizer relies on simple heuristics, for example:
All contiguous strings of alphabetic characters are part of one token; likewise with numbers.
Tokens are separated by whitespace characters, such as a space or line break, or by punctuation characters.
Punctuation and whitespace may or may not be included in the resulting list of tokens.
In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as contractions, hyphenated words, emoticons, and larger constructs such as URIs (which for some purposes may count as single tokens). A classic example is "New York-based", which a naive tokenizer may break at the space even though the better break is (arguably) at the hyphen.
Ancient Greek was often written in scriptio continua, with no spaces between words.
Tokenization is particularly difficult for languages such as Chinese which have no word boundaries.[1]
Some ways to address the more difficult problems include developing more complex heuristics, querying a table of common special-cases, or fitting the tokens to a language model that identifies collocations in a later processing step.
A deeper form or Tokenization is MicroTokenization or Micro-Tokenization. Micro-Tokenization is defined as deep, pinpoint, individualized processing of data combined with various proprietary AES algorithm designs up to and including 1024-bit cipher keys tokenizing the data up to 2 GB in size, without utilizing any of the original sensitive data to derive the token from while preserving speed, security, and portability. Micro-Tokenization techniques are the newest method of information security and innovative methodology for data protection. It has evolved data protection technology to new levels never before accomplished. It works off an entirely different premise. Just the opposite of “Bulk Encryption”, the MicroTokenization approach protects sensitive data individually, even down to the “Byte” level if that is what is desired and or programmed. Micro-Tokenization maintains speed and accessibility while providing the highest level of security known to the public sector. This modular technology is completely scalable, adaptable, and accessible, and has been designed to meet future needs and evolve with the new best practices in the security world. The core elements of the technology follow along with long enforced and tried methods used by the US Armed Forces and technology powerhouses, but put together in a new innovative and proprietary way. By Micro-Tokenizing data, the data remains both usable while simultaneously exceeding industry security standards as well as regulation
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