What is Stemming and Lemmatization?
Stemming and lemmatization are two crucial techniques in Natural Language Processing (NLP) used to reduce words to their base or root form. These methods are particularly important in text mining and search engines, where understanding the essence of words enhances comprehension and retrieval accuracy.
Stemming
Stemming involves trimming a word to its base or stem form, which may not always be a valid word. For instance, the word "running" is stemmed to "run," and "better" might be reduced to "better." The primary goal of stemming is to reduce inflected words to their root form, typically using algorithms such as the Porter Stemmer or Snowball Stemmer, which apply a set of rules to cut off prefixes or suffixes from words.
Lemmatization
Unlike stemming, lemmatization aims to reduce a word to its meaningful base form, known as a lemma, which is always a valid word. For example, the lemma for "running" is "run," and for "better," it is "good." Lemmatization considers the context of the word within the sentence, often requiring a dictionary and part-of-speech tagging to properly understand the intended meaning.
Comparison
While both techniques aim to simplify words, stemming can sometimes produce non-words, whereas lemmatization always yields valid words. Consequently, lemmatization is usually more accurate yet computationally heavier than stemming, making the choice between them context-dependent.