Tokenization is the process of breaking up a string of text into smaller pieces, called tokens. In the context of text analytics, tokenization usually refers to the process of breaking up a string of text into individual words, called word tokens. However, tokenization can also refer to the process of breaking up a string of text into other meaningful units, such as sentences or paragraphs.
Tokenization is a type of text pre-processing that is often used as the first step in text analytics pipelines. Tokenization can be performed using a variety of algorithms, including rule-based methods and statistical methods. It is also often used as a pre-processing step for other text analytics tasks, such as part-of-speech tagging, named entity recognition, and topic modeling. It is also sometimes used as a pre-processing step for machine learning tasks, such as text classification and text clustering.
Methods of Tokenization
There are two main methods of tokenization: rule-based methods and statistical methods.
Rule-based methods are typically designed specifically for a particular language or writing system. For example, there are rule-based tokenizers for English, Spanish, Chinese, and other languages. These tokenizers typically use a set of rules to identify word boundaries in a string of text.
Statistical methods for tokenization are based on the statistical properties of a language. These methods are usually language-independent, which means they can be used to tokenize text in any language. Statistical methods are often more accurate than rule-based methods, but they can be more computationally expensive.
Advantages and Disadvantages of Tokenization
Tokenization is a simple and effective way to pre-process text data. It can be used to break up a string of text into smaller pieces, which can make it easier to work with. Tokenization can also be used to prepare text data for other text analytics tasks, such as part-of-speech tagging and named entity recognition.
However, tokenization is not without its disadvantages. One disadvantage of tokenization is that it can sometimes create very long strings of text, which can be difficult to work with. Another disadvantage is that it can sometimes create duplicate tokens, which can lead to confusion when performing other text analytics tasks. Finally, tokenization can sometimes remove important information from a string of text, such as punctuation or capitalization.