Text Processing Pipeline Tokenization Tokenization is the process of breaking down a text into its individual words or units. This can be done using tec...
Text Processing Pipeline
Tokenization
Tokenization is the process of breaking down a text into its individual words or units. This can be done using techniques such as splitting on spaces, tabs, and new lines, or using regular expressions.
Stop Words
Stop words are common words that occur frequently but add little meaning to the text, such as "the," "a," "is," "a," and "of." Stop words can be removed to improve the performance of natural language processing (NLP) models by reducing the amount of time spent on processing them.
Stemming
Stemming is a process of reducing words to their root form. For example, the words "running," "ran," "runs," and "run" would all be stemmed to the root word "run." Stemming can be done using techniques such as using a stemmer algorithm or by manually inspecting the words in a text.
Lemmatization
Lemmatization is a process of classifying words into their grammatical category (e.g., noun, verb, adjective). For example, the words "running," "ran," and "runs" would all be lemmatized to the same grammatical category: "verb." Lemmatization can be done using techniques such as using a dictionary lookup or by manually examining the words in a text.
These are just the basic steps of a text processing pipeline. By following these steps, we can prepare a text for further analysis and processing by NLP models