Stemming in Natural Language Processing (NLP) is the process of reducing words to their stem or root form. This is done in order to create a normalized database of words to improve the speed and accuracy of searches related to language processing.
Stemming is usually done using an algorithm containing a set of instructions for changing words into the stem form. The operation of a stemmer is dependent on the context of words and the language of the text being processed. The goal of a stemmer is to reduce a word to its base form to avoid the need to search for multiple variations of a word.
Processes of stemming generally involve the removing of prefixes and suffixes along with any tense changes such as, -ed at the end of a word. Stemming algorithms may also perform other types of manipulations such as checks for word inflection. For example, “run” and “running” would be reduced to “run” if the rules of the algorithm allowed for the dropping of “ing” off the end of the word.
Stemming is widely used in many natural language processing applications such as text retrieval and information retrieval, text classification, information extraction, etc. Stemming algorithms are important for allowing these applications to make more accurate searches that are better tuned to everyday language used by people.
Stemming is also used in natural language understanding which is the process of understanding written or spoken language. This type of application helps computer systems understand what people are saying by reducing words to their root forms so that they can be more easily understood by the computer. Stemming is often used in combination with other techniques such as lemmatization.
Overall, stemming in natural language processing is an important step in language processing applications that is used to create a more accurate search database while improving speed and accuracy. It is a critical component of many natural language understanding and machine learning applications.
