public interface Tokenizer
Tokenization is a necessary step before more complex NLP tasks can be applied, these usually process text on a token level. The quality of tokenization is important because it influences the performance of high-level task applied to it.
In segmented languages like English most words are segmented by white spaces expect for punctuations, etc. which is directly attached to the word without a white space in between, it is not possible to just split at all punctuations because in abbreviations dots are a part of the token itself. A tokenizer is now responsible to split these tokens correctly.
In non-segmented languages like Chinese tokenization is more difficult since words are not segmented by a whitespace.
Tokenizers can also be used to segment already identified tokens further into more atomic parts to get a deeper understanding. This approach helps more complex task to gain insight into tokens which do not represent words like numbers, units or tokens which are part of a special notation.
For most further task it is desirable to over tokenize rather than under tokenize.
s- The string to be tokenized.
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