Stemming, in literal terms, is the process of cutting down the branches of a tree to its stem. So effectively, with the use of some basic rules, any token can be cut down to its stem. Stemming is more of a crude rule-based process by which we want to club together different variations of the token. For example, the word eat will have variations like eating, eaten, eats, and so on. In some applications, as it does not make sense to differentiate between eat and eaten, we typically use stemming to club both grammatical variances to the root of the word. While stemming is used most of the time for its simplicity, there are cases of complex language or complex NLP tasks where it's necessary to use lemmatization instead. Lemmatization is a more robust and methodical way of combining grammatical variations to the root of a word.
In the following snippet, we show a few stemmers:
>>>from nltk.stem import PorterStemmer # import Porter stemmer >>>from nltk.stem.lancaster...