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Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing .
Example of the UDH for an sms split into two parts: 05 00 03 CC 02 01 [ message ] 05 00 03 CC 02 02 [ message ] Note if a UDH is present and the data encoding is the default 7-bit alphabet, the user data must be 7-bit word aligned after the UDH. [2] This means up to 6 bits of zeros need to be inserted at the start of the [message].
For most spoken languages, the boundaries between lexical units are difficult to identify; phonotactics are one answer to this issue. One might expect that the inter-word spaces used by many written languages like English or Spanish would correspond to pauses in their spoken version, but that is true only in very slow speech, when the speaker deliberately inserts those pauses.
At the end of a line, a word is separated in writing into parts, conventionally called "syllables", if it does not fit the line and if moving it to the next line would make the first line much shorter than the others. This can be a particular problem with very long words, and with narrow columns in newspapers.
For example, in the text string: The quick brown fox jumps over the lazy dog. the string is not implicitly segmented on spaces, as a natural language speaker would do. The raw input, the 43 characters, must be explicitly split into the 9 tokens with a given space delimiter (i.e., matching the string " "or regular expression /\s{1}/).
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A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.