What is the difference between lemmatization vs stemming?

Stemming just removes or stems the last few characters of a word, often leading to incorrect meanings and spelling. Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma. Sometimes, the same word can have multiple different Lemmas. We should identify the Part of Speech (POS) tag for the word in that specific context. Here are the examples to illustrate all the differences and use cases:

1. If you lemmatize the word 'Caring', it would return 'Care'. If you stem, it would return 'Car' and this is erroneous.
2. If you lemmatize the word 'Stripes' in verb context, it would return 'Strip'. If you lemmatize it in noun context, it would return 'Stripe'. If you just stem it, it would just return 'Strip'.
3. You would get same results whether you lemmatize or stem words such as walking, running, swimming... to walk, run, swim etc.
4. Lemmatization is computationally expensive since it involves look-up tables and what not. If you have large dataset and performance is an issue, go with Stemming. Remember you can also add your own rules to Stemming. If accuracy is paramount and dataset isn't humongous, go with Lemmatization.

Stemming is the process of removing the last few characters of a given word, to obtain a shorter form, even if that form doesn't have any meaning.

Examples,

"beautiful" -> "beauti"
"corpora" -> "corpora"

Stemming can be done very quickly.

Lemmatization on the other hand, is the process of converting the given word into it's base form according to the dictionary meaning of the word.

Examples,

"beautiful" -> "beauty"
"corpora" -> "corpus"

Lemmatization takes more time than stemming.

Huang et al. describes the Stemming and Lemmatization as the following. The selection depends upon the problem and computational resource availability.

Stemming identifies the common root form of a word by removing or replacing word suffixes (e.g. “flooding” is stemmed as “flood”), while lemmatization identifies the inflected forms of a word and returns its base form (e.g. “better” is lemmatized as “good”).

Huang, X., Li, Z., Wang, C., & Ning, H. (2020). Identifying disaster related social media for rapid response: a visual-textual fused CNN architecture. International Journal of Digital Earth, 13(9), 1017–1039. https://doi.org/10.1080/17538947.2019.1633425

Stemming and Lemmatization both generate the foundation sort of the inflected words and therefore the only difference is that stem may not be an actual word whereas, lemma is an actual language word.

Stemming follows an algorithm with steps to perform on the words which makes it faster. Whereas, in lemmatization, you used a corpus also to supply lemma which makes it slower than stemming. you furthermore might had to define a parts-of-speech to get the proper lemma.

The above points show that if speed is concentrated then stemming should be used since lemmatizers scan a corpus which consumes time and processing. It depends on the problem you’re working on that decides if stemmers should be used or lemmatizers. for more info visit the link: https://towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221

Stemming is the process of producing morphological variants of a root/base word. Stemming programs are commonly referred to as stemming algorithms or stemmers. Often when searching text for a certain keyword, it helps if the search returns variations of the word. For instance, searching for “boat” might also return “boats” and “boating”. Here, “boat” would be the stem for [boat, boater, boating, boats].

Lemmatization looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words. The lemma of ‘was’ is ‘be’ and the lemma of ‘mice’ is ‘mouse’.

I did refer this link, https://towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221

In short, the difference between these algorithms is that only lemmatization includes the meaning of the word in the evaluation. In stemming, only a certain number of letters are cut off from the end of the word to obtain a word stem. The meaning of the word does not play a role in it.

In short:

Lemmatization: uses context to transform words to their dictionary(base) form also known as Lemma

Stemming: uses the stem of the word, most of the time removing derivational affixes.

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