In computational linguistics, lemmatisation is the algorithmic process of determining the lemma for a given word. Since the process may involve complex tasks such as understanding context and determining the part of speech of a word in a sentence (requiring, for example, knowledge of the grammar of a language) it can be a hard task to implement a lemmatiser for a new language.
In many languages, words appear in several inflected forms. For example, in English, the verb 'to walk' may appear as 'walk', 'walked', 'walks', 'walking'. The base form, 'walk', that one might look up in a dictionary, is called the lemma for the word. The combination of the base form with the part of speech is often called the lexeme of the word.
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.
- 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.
Analysers like Lucene Snowball store the base stemmed format of the word without the knowledge of meaning, but taking into account the semantics of the word formation only. The stemmed word itself might not be a valid word: 'lazy', as seen in the example below, is stemmed by many stemmers to 'lazi'. This is because the purpose of stemming is not to produce the appropriate lemma – that is a more challenging task that requires knowledge of context. The main purpose of stemming is to map different forms of a word to a single form, and as a relatively simple, rules-based algorithm, it makes the above-mentioned sacrifice to ensure that, for example, when 'laziness' is stemmed to 'lazi', it has the same stem as 'lazy'.
Use of lemmatisation in biomedicine 
Morphological analysis of published biomedical literature can yield useful results. Morphological processing of biomedical text can be more effective by a specialised lemmatisation program for biomedicine, and may improve the accuracy of practical information extraction tasks.
- Collins English Dictionary, entry for "lemmatise"
- "Lucene Snowball". Apache project.
- Martin Porter. "Porter Stemmer".
- Liu, H.; Christiansen, T.; Baumgartner, W. A.; Verspoor, K. (2012). "BioLemmatizer: A lemmatization tool for morphological processing of biomedical text". Journal of Biomedical Semantics 3: 3. doi:10.1186/2041-1480-3-3. PMC 3359276. PMID 22464129.
|Look up lemmatisation in Wiktionary, the free dictionary.|
- Collatinus, an open-source lemmatiser for latin language
- Apache Lucene core
- Lemmatizer.org — an open-source lemmatizer of English and Russian languages
- MorphAdorner , a Java open-source lemmatiser for English
- A lemmatizer for Spanish language
- A lemmatizer for Estonian language
- Rosette linguistics platform: A full commercial morphological analyzer with support for 38 Asian, European, and Middle Eastern languages
- Public API for Spanish lemmatisation