Lemmatization is the grouping of all inflected forms of a word to be analyzed as a single item. The groups in lemmatization are identified by their base root dictionary form which is known as a lemma. Lemmatization is important in natural language understanding (NLU) and natural language processing (NLP).
Lemmatization is a part of linguistic studies in the morphology of words used in artificial intelligence (AI) information retrieval and extraction. Stemming is a more basic method of reversing the inflectional forms to a base word by stripping prefixes and suffixes. Both methods are a part of queries and search engine functions.
In information extraction, AI uses lemmatization to find more information related to a term than what would be found by just its lemma. Searching by inflectional forms of words is a part of extracting meaningful information from vast sources like big data or the Internet. This makes sure that additional forms of a word related to a subject may need to be searched to get the most and best results.
At its simplest, a stemming algorithm may just strip recognized prefixes and suffixes. However, that simplicity can lead to the word laziness being reduced to
Lemmatization, on the other hand, is more complex. Words are broken down into a part-of-speech (the categories of word types) by way of the rules of grammar. Lemmatization uses the rules of morphology in linguistics along with a vocabulary to take words as used in speech and writing, gathering their inflected forms.
In lemmatization, these gathered inflected forms are analyzed to understand the group as a whole. When an inflectional form of a word is searched, often only direct mentions of the search form might be found. The same is true if the lemma is searched without inflectional forms – searching for all forms is more likely to access the most information.