Stemming in NLP and deep learning

Stemming

Stemmization is the process of bringing a word to its root.

Brings different variations of a word (eg "help", "help", "helped", "useful") to its original form (eg "help"), removes all word attachments (prefix, suffix, ending) and leaves only the root the words.

The root of the word may or may not be an existing word in the language. For example, "mov" is the root of the word "movie", "emo" is the root of the word "emotion".

Lemmatization

The lemmatization is similar to the stemmatization in the sense that it returns a word to its original form, but with one difference: in this case, the root of the word will be the word that exists in the language. For example, the word "cura" will end with "cura" rather than "auto", as in stemming: doctranslator.

WordNet is a database of words that exist in the English language. Lemmatizer from NLTK WordNetLemmatizer () uses words from WordNet.

N-grams are combinations of several words used together, N-grams, where N = 1, are called unigrams. Similarly, you can continue with bigrams (N = 2), trigrams (N = 3) and so on.

N-grams can be used when we need to store some kind of data sequence, for example, which word most often follows a given word. Unigrams contain no data sequence, as each word is taken individually.