Uses of stemming and lemmatization
Stemming and lemmatization are both a form of NLP that can be used to extract information from text. This is known as text mining. Text mining tasks come in a variety of categories, including text clustering, categorization, summarizing documents, and sentiment analysis. Stemming and lemmatization can be used in conjunction with deep learning to solve some of these tasks, as we will see later in this book.
By performing preprocessing using stemming and lemmatization, coupled with the removal of stop words, we can better reduce our sentences to understand their core meaning. By removing words that do not significantly contribute to the meaning of the sentence and by reducing words to their roots or lemmas, we can efficiently analyze sentences within our deep learning frameworks. If we are able to reduce a 10-word sentence to five words consisting of multiple core lemmas rather than multiple variations of similar words, this means much less data...
