The goal of both stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form.
However, the two words differ in their flavor. Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma .
From the NLTK docs:
Lemmatization and stemming are special cases of normalization. They identify a canonical representative for a set of related word forms.
Lemmatisation is closely related to stemming. The difference is that a
stemmer operates on a single word without knowledge of the context,
and therefore cannot discriminate between words which have different
meanings depending on part of speech. However, stemmers are typically
easier to implement and run faster, and the reduced accuracy may not
matter for some applications.
For instance:
The word “better” has “good” as its lemma. This link is missed by
stemming, as it requires a dictionary look-up.
The word “walk” is the base form for word “walking”, and hence this
is matched in both stemming and lemmatisation.
The word “meeting” can be either the base form of a noun or a form
of a verb (“to meet”) depending on the context, e.g., “in our last
meeting” or “We are meeting again tomorrow”. Unlike stemming,
lemmatisation can in principle select the appropriate lemma
depending on the context.
A stemmer will return the stem of a word, which needn’t be identical to the morphological root of the word. It usually sufficient that related words map to the same stem,even if the stem is not in itself a valid root, while in lemmatisation, it will return the dictionary form of a word, which must be a valid word.
In lemmatisation, the part of speech of a word should be first determined and the normalisation rules will be different for different part of speech, while the stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech.
The purpose of both stemming and lemmatization is to reduce morphological variation. This is in contrast to the the more general “term conflation” procedures, which may also address lexico-semantic, syntactic, or orthographic variations.
The real difference between stemming and lemmatization is threefold:
Stemming reduces word-forms to (pseudo)stems, whereas lemmatization reduces the word-forms to linguistically valid lemmas. This difference is apparent in languages with more complex morphology, but may be irrelevant for many IR applications;
Lemmatization deals only with inflectional variance, whereas stemming may also deal with derivational variance;
In terms of implementation, lemmatization is usually more sophisticated (especially for morphologically complex languages) and usually requires some sort of lexica. Satisfatory stemming, on the other hand, can be achieved with rather simple rule-based approaches.
Lemmatization may also be backed up by a part-of-speech tagger in order to disambiguate homonyms.
As MYYN pointed out, stemming is the process of removing inflectional and sometimes derivational affixes to a base form that all of the original words are probably related to. Lemmatization is concerned with obtaining the single word that allows you to group together a bunch of inflected forms. This is harder than stemming because it requires taking the context into account (and thus the meaning of the word), while stemming ignores context.
As for when you would use one or the other, it’s a matter of how much your application depends on getting the meaning of a word in context correct. If you’re doing machine translation, you probably want lemmatization to avoid mistranslating a word. If you’re doing information retrieval over a billion documents with 99% of your queries ranging from 1-3 words, you can settle for stemming.
As for NLTK, the WordNetLemmatizer does use the part of speech, though you have to provide it (otherwise it defaults to nouns). Passing it “dove” and “v” yields “dive” while “dove” and “n” yields “dove”.
An example-driven explanation on the differenes between lemmatization and stemming:
Lemmatization handles matching “car” to “cars” along
with matching “car” to “automobile”.
Stemming handles matching “car” to “cars” .
Lemmatization implies a broader scope of fuzzy word matching that is
still handled by the same subsystems. It implies certain techniques
for low level processing within the engine, and may also reflect an
engineering preference for terminology.
[…] Taking FAST as an example,
their lemmatization engine handles not only basic word variations like
singular vs. plural, but also thesaurus operators like having “hot”
match “warm”.
This is not to say that other engines don’t handle synonyms, of course
they do, but the low level implementation may be in a different
subsystem than those that handle base stemming.
ianacl
but i think Stemming is a rough hack people use to get all the different forms of the same word down to a base form which need not be a legit word on its own
Something like the Porter Stemmer can uses simple regexes to eliminate common word suffixes
Lemmatization brings a word down to its actual base form which, in the case of irregular verbs, might look nothing like the input word
Something like Morpha which uses FSTs to bring nouns and verbs to their base form
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:
If you lemmatize the word ‘Caring‘, it would return ‘Care‘. If you stem, it would return ‘Car‘ and this is erroneous.
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‘.
You would get same results whether you lemmatize or stem words such as walking, running, swimming… to walk, run, swim etc.
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.
