Lemmatization (or less commonlylemmatisation) inlinguistics is the process of grouping together theinflected forms of a word so they can be analysed as a single item, identified by the word'slemma, or dictionary form.[1]
Incomputational linguistics, lemmatization is the algorithmic process of determining thelemma of a word based on its intended meaning. Unlikestemming, lemmatization depends on correctly identifying the intendedpart of speech and meaning of a word in a sentence, as well as within the largercontext surrounding that sentence, such as neighbouring sentences or even an entire document. As a result, developing efficient lemmatization algorithms is an open area of research.[2][3][4]
In many languages, words appear in severalinflected forms. For example, in English, the verb 'to walk' may appear as 'walk', 'walked', 'walks' or 'walking'. The base form, 'walk', that one might look up in a dictionary, is called thelemma for the word. The association of the base form with a part of speech is often called alexeme of the word.
Lemmatization is closely related tostemming. The difference is that a stemmer operates on a single wordwithout 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. The reduced "accuracy" may not matter for some applications. In fact, when used within information retrieval systems, stemming improves queryrecall accuracy, or true positive rate, when compared to lemmatization. Nonetheless, stemming reducesprecision, or the proportion of positively-labeled instances that are actually positive, for such systems.[5]
For instance:
Document indexing software likeLucene[6] can store the base stemmed format of the word without the knowledge of meaning, but only considering word formation grammar rules. 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.[7] As a rule-based algorithm, dependent only upon the spelling of a word, it sacrifices accuracy to ensure that, for example, when 'laziness' is stemmed to 'lazi', it has the same stem as 'lazy'.
A trivial way to do lemmatization is by simple dictionary lookup. This works well for straightforward inflected forms, but arule-based system will be needed for other cases, such as in languages with longcompound words. Such rules can be either hand-crafted or learned automatically from anannotatedcorpus.
Morphological analysis of published biomedical literature can yield useful results. Morphological processing of biomedical text can be more effective by a specialized lemmatization program for biomedicine, and may improve the accuracy of practicalinformation extraction tasks.[8]
