In the world of Natural Language Processing, there are two main methods for dealing with words: stemming and lemmatization. Stemming simply removes or “stems” the last few characters of a word, often leading to incorrect meanings and spelling. Lemmatization, on the other hand, considers the context and converts the word to its meaningful base form, which is called a lemma. Sometimes, the same word can have multiple different lemmas.
Stemming and keyword stemming refer to analyzing the meaning behind a word and comprehending the different word forms of a particular search query. It’s called stemming because it stems from the word stem, base, or root form.
Example: Buy >> Buying, Bought, Buys
In Stemming, you focus on the root topic and work your way out to variations of the word.
Lemmatization is the process of grouping the inflected forms of a word in order to analyze them as a single word in linguistics.
Example: Buying, Bought, Buys >> Buy
In Lemmatization, you find the root topic by analyzing the keyword variations.
So, which is better for NLP: stemming or lemmatization? The answer is…it depends. Both methods have their pros and cons, and the best option for your application will likely depend on the specific language you’re working with and the task at hand. In general, though, lemmatization is often seen as being more accurate than stemming, since it takes into account the context of a word in order to correctly identify its base form. If you’re looking for a more precise way to deal with words in your NLP applications, then lemmatization is probably the way to go if you want to identify seed keywords, and stemming to create a keyword idea list.
Published on: 2022-03-28
Updated on: 2022-05-12