Word stemming is the process of reducing a word to its base form, typically for the purpose of analysis. For example, the word “stemming” could be reduced to “stem.” This is often done with inflected languages, where a single word can have multiple forms.
One common use of word stemming is in information retrieval, where it can be used to normalize search terms. For example, if a user searches for “cats,” stemming could be used to also return results for “cat.”
Outside of text analytics, word stemming is sometimes used in natural language processing applications such as machine translation and automatic summarization.
Word stemming should not be confused with lemmatization, which is a related but different process. While word stemming typically involves chopping off suffixes to arrive at a base form, lemmatization usually involves determining the root form of a word based on its meaning. For example, the words “better” and “good” would both be reduced to “good.”
When to use word stemming?
There are a few different reasons why you might want to use word stemming. One common reason is for information retrieval, as it can help normalize search terms. This is especially useful for inflected languages, where a single word can have multiple forms.
Another reason to use word stemming is for machine translation and automatic summarization. In these applications, it can be helpful to reduce a word to its base form in order to more accurately determine its meaning.
When not to use word stemming?
There are a few situations where you might not want to use word stemming. One reason is if you need to preserve the original word form for some reason. For example, if you were indexing a document for search, you would want to preserve the original word forms in order to return results that match the user’s query.
Another reason not to use word stemming is if you are working with a language that doesn’t have inflection, such as Chinese. In this case, there would be no need to reduce words to their base form.