What is the difference between stemming and lemmatization

What is the real difference between lemmatization and stemming? - Python, nlp, nltk, lemmatization

When do I use each one?

Also ... is NLTK lemmatization dependent on parts of speech? Wouldn't it be more accurate if it were?

Reply:

83 for the answer № 1

In a nutshell: http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html

The goal of stemming and lemmatization is to reduce inflected forms and sometimes derivative forms of a word to a common base form.

However, the two words differ in their taste. Stemming usually refers to a crude heuristic process of cutting off the ends of words in hopes of mostly getting that goal right, and often also removing derivative elements. Lemmatization usually refers to doing the right thing with a vocabulary and a morphological analysis of words. Usually it aims to just remove inflectional ends and return the base or dictionary form of a word known as a lemma.

From the NLTK documents:

Lemmatization and stemming are special cases of normalization. They identify a canonical representative for a number of related word forms.


42 for the answer № 2

lemmatization is closely related to stemming. The difference is that a Stemmer works on a single word without knowing the context and therefore cannot distinguish between words that have different meanings depending on a part of the language. However, stemmers are typically easier to implement and faster to run, and the reduced accuracy may not be an issue for some applications.

For example:

  1. The word "better" has "good" as a lemma. This link is missed by stemming as it requires a dictionary search.

  2. The word "go" is the basic form for the word "go" and therefore this is also coordinated in stemming and lemmatization.

  3. The word "meeting" can be either the base form of his noun or some form of a verb ("to meet") depending on the context, e.g. B. "In our last meeting" or "We will meet again tomorrow". In contrast to stemming, lemmatization can basically select the appropriate lemma depending on the context.

source: https://en.wikipedia.org/wiki/Lemmatisation


12 for the answer № 3

The purpose of stemming and lemmatization is to reduce morphological variation. This is in contrast to the more general "Term Conflation" procedures, which can also refer to lexico-semantic, syntactic or orthographic variations.

The real difference between stemming and lemmatization is threefold:

  1. Stemming reduces word forms to (pseudo) stems, while lemmatization reduces the word forms to linguistically valid lemmas. This difference is evident in languages ​​with more complex morphology, but may be irrelevant to many IR applications.

  2. Lemmatization only deals with inflection variance, while withholding can also affect lead variance.

  3. In terms of implementation, lemmatization is usually more demanding (especially for morphologically complex languages) and usually requires some kind of lexicons. Satisfied stemming, on the other hand, can be achieved with rather simple rule-based approaches.

Lemmatization can also be supported by a partial word tagger to disambiguate homonyms.


11 for the answer № 4

As MYYN has pointed out, stemming is the process of removing inflected and sometimes derivative affixes to a base form that all original words refer to. Lemmatization is about getting a single word that you can use to group multiple curved shapes together. This is more difficult than stemming because the context (and therefore the meaning of the word) needs to be taken into account while stemming ignores context.

When would you use one or the other is a question of how much your application depends on whether the meaning of a word is correct in context. When doing machine translation, you probably want lemmatization to prevent the words from being translated incorrectly If you're looking at information on a billion documents and 99% of your queries consist of 1-3 words, you can be satisfied with the stemming.

As for NLTK, the WordNetLemmatizer uses the part of the language, although you have to provide it (otherwise it will be set to nouns). Passing "Taube" and "v" results in "diving", while "Taube" and "n" result in "Taube".


8 for the answer № 5

There are two aspects to show their differences:

  1. A Stemmer will return the stem of a word, which need not be identical to the morphological root of the word. Usually it is sufficient that related words are assigned to the same stem, even if the stem is not in itself a valid stem lemmatizationreturns the dictionary form of a word that must be a valid word.

  2. in the lemmatizationThe word part of a word should be determined first and the normalization rules will be different for different word parts while the Stemmer works with a single word without knowing the context and therefore cannot distinguish between words that have different meanings depending on the part of speech.

Reference http://textminingonline.com/dive-into-nltk-part-iv-stemming-and-lemmatization


5 for the answer № 6

An exemplary explanation of the differences 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 spectrum of fuzzy-fitting words for it is still handled by the same subsystems. It implies certain techniques for low-level processing within the engine and may also have a technical preference for terminology.

[...] Using the example of FAST, your lemmatization engine not only masters basic word variations such as singular vs. plural, but also thesaurus operators such as "hot", game "warm".

This doesn't mean that other engines don't treat synonyms of course, they do, but the low-level implementation may be in a different subsystem than those that handle basic parenting.

http://www.ideaeng.com/stemming-lemmatization-0601


1 for the answer № 7

Ianacl
But I think stemming is a tough hack that people use to base all of the different forms of the same word into one that doesn't have to be a real word
Something like the Porter Stemmer can use simple regex to remove suffixes from common words

Lemmatization brings a word back to its actual basic form, which in the case of irregular verbs may not resemble the word entered
Something like morpha, which uses FSTs to bring nouns and verbs into their basic form