![]() Performing this text-processing technique is often useful for dealing with sparsity and/or standardizing vocabulary. For example, the words “programming,” “programmer,” and “programs” can all be reduced down to the common word stem “program.” In other words, “program” can be used as a synonym for the prior three inflection words. Stemming is a technique used to reduce an inflected word down to its word stem. This tutorial will cover stemming and lemmatization from a practical standpoint using the Python Natural Language ToolKit (NLTK) package.Ĭheck out this DataCamp Workspace to follow along with the code. Researchers have studied these techniques for years NLP practitioners typically use them to prepare words, text, and documents for further processing in a number of tasks. ![]() Two popular text normalization techniques in the field of Natural Language Processing (NLP), the application of computational techniques to analyze and synthesize natural language and speech, are stemming and lemmatization. This helps reduce randomness and bring the words in the corpus closer to the predefined standard, improving the processing efficiency since the computer has fewer features to deal with. When working with text, sometimes it’s necessary to apply normalization techniques to get words to their root form from their derived versions. “In linguistic morphology, inflection is a process of word formation, in which a word is modified to express different grammatical categories such as tense, case, voice, aspect, person, number, gender, mood, animacy, and definiteness.” All inflected languages consist of words with common root forms, but the degree of inflection varies based on the language. ![]() This means there are many words in English derived from another word for example, the inflected word “normality” is derived from the word “norm,” which is the root form. The modern English language is considered a weakly inflected language.
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