Segmentation, in the context of text analytics, refers to the process of dividing a text into smaller parts, called segments.
Importance of Segmentation
Segmentation can be used for a variety of purposes, including:
- To improve the accuracy of text analytics models by providing more granular data
- To make text analytics models more efficient by reducing the amount of data that needs to be processed
- To make text analytics results more interpretable by providing context for individual results
Segmentation can be performed at various levels of granularity, from individual words to entire documents. The level of granularity that is used will depend on the purpose of the segmentation and the type of text being analyzed.
Segmentation vs. Tokenization
When comparing Segmentation to similar terms, it is important to note that:
- Segmentation is a process, while tokenization is a result of that process. Tokenization is the process of breaking a text down into smaller units called tokens, and Segmentation is the process of dividing a text into smaller parts.
- Segmentation can be performed at different levels of granularity, while tokenization always results in tokens of the same size (usually words).
- Segmentation is typically used to improve the accuracy or efficiency of text analytics models, while tokenization is typically used to make text analytics results more interpretable.
Segmentation vs. N-gram Segmentation
When comparing Segmentation to n-gram segmentation, it is important to note that:
- Segmentation is a process, while n-gram segmentation is a result of that process. N-gram segmentation is the process of dividing a text into smaller parts called n-grams.
- Segmentation can be performed at different levels of granularity, while n-gram segmentation always results in n-grams of the same size (usually words).
- Segmentation is typically used to improve the accuracy or efficiency of text analytics models, while n-gram segmentation is typically used to make text analytics results more interpretable.