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Lemmatization

What is lemmatization?

Lemmatization is a text normalization technique used in Natural Language Processing (NLP). It has been studied for a very long time and lemmatization algorithms have been made since the 1960s.

Tagging systems, indexing, SEOs, information retrieval, and web search all use lemmatization to a vast extent. Lemmatization usually involves using a vocabulary and morphological analysis of words, removing inflectional endings, and returning the dictionary form of a word (the lemma).

What is lemmatization used for?

Lemmatization is among the best ways to help chatbots understand your customers’ queries to a better extent. Since this involves a morphological analysis of the words, the chatbot can understand the contextual form of the words in the text and can gain a better understanding of the overall meaning of the sentence that is being lemmatized.

What is the difference between stemming and lemmatization?

While stemming and lemmatization both focus on attempting to reduce the inflectional form of each word into a common base or root, they are not the same. 

They work in different ways, which means that the result that they return differs.

In stemming, the end or beginning of a word is cut off, keeping common prefixes and suffixes that can be found in inflected words in mind. Lemmatization uses dictionaries to conduct a morphological analysis of the word and link it to its lemma.

Lemmatization involves greater complexity than stemming. This is because the process needs the words to be classified by a part-of-speech and the inflected form. This can be quite a difficult task in any language other than English.

One stem can be common for inflectional forms of many lemmas and the same lemma can be linked to forms with different stems.


Why is lemmatization important?

Lemmatization is a vital part of Natural Language Understanding (NLU) and Natural Language  Processing (NLP). It plays critical roles both in Artificial Intelligence (AI) and big data analytics.

Lemmatization is extremely important because it is far more accurate than stemming. This brings great value when working with a chatbot where it is crucial to understand the meaning of a user’s messages.

The major disadvantage to lemmatization algorithms, however, is that it they are much slower than stemming algorithms.

Applications of lemmatization

Here are some of the areas in which lemmatization can be used, other than in chatbots. 

Sentiment analysis

This refers to an analysis of people’s messages, reviews, or comments to understand how they feel about something. Before the text is analyzed, it is lemmatized.


Information Retrieval Environments

Lemmatizing is used for mapping documents to common topics and displaying search results. To do so, it indexes when documents are increasing to large numbers.


Biomedicine

Lemmatization can be used while morphologically analyzing biomedical literature. The Biolemmatizer tool has been been for this very purpose. It pulls lemmas based on the use of a word lexicon. But if the word is not found in the lexicon, it defines rules that turn the word into a lemma. This tool has been 97.5% accurate in its attempts to lemmatize an evaluation set prepared from the CRAFT corpus.



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Lemmatization

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is lemmatization?

Lemmatization is a text normalization technique used in Natural Language Processing (NLP). It has been studied for a very long time and lemmatization algorithms have been made since the 1960s.

Tagging systems, indexing, SEOs, information retrieval, and web search all use lemmatization to a vast extent. Lemmatization usually involves using a vocabulary and morphological analysis of words, removing inflectional endings, and returning the dictionary form of a word (the lemma).

What is lemmatization used for?

Lemmatization is among the best ways to help chatbots understand your customers’ queries to a better extent. Since this involves a morphological analysis of the words, the chatbot can understand the contextual form of the words in the text and can gain a better understanding of the overall meaning of the sentence that is being lemmatized.

What is the difference between stemming and lemmatization?

While stemming and lemmatization both focus on attempting to reduce the inflectional form of each word into a common base or root, they are not the same. 

They work in different ways, which means that the result that they return differs.

In stemming, the end or beginning of a word is cut off, keeping common prefixes and suffixes that can be found in inflected words in mind. Lemmatization uses dictionaries to conduct a morphological analysis of the word and link it to its lemma.

Lemmatization involves greater complexity than stemming. This is because the process needs the words to be classified by a part-of-speech and the inflected form. This can be quite a difficult task in any language other than English.

One stem can be common for inflectional forms of many lemmas and the same lemma can be linked to forms with different stems.


Why is lemmatization important?

Lemmatization is a vital part of Natural Language Understanding (NLU) and Natural Language  Processing (NLP). It plays critical roles both in Artificial Intelligence (AI) and big data analytics.

Lemmatization is extremely important because it is far more accurate than stemming. This brings great value when working with a chatbot where it is crucial to understand the meaning of a user’s messages.

The major disadvantage to lemmatization algorithms, however, is that it they are much slower than stemming algorithms.

Applications of lemmatization

Here are some of the areas in which lemmatization can be used, other than in chatbots. 

Sentiment analysis

This refers to an analysis of people’s messages, reviews, or comments to understand how they feel about something. Before the text is analyzed, it is lemmatized.


Information Retrieval Environments

Lemmatizing is used for mapping documents to common topics and displaying search results. To do so, it indexes when documents are increasing to large numbers.


Biomedicine

Lemmatization can be used while morphologically analyzing biomedical literature. The Biolemmatizer tool has been been for this very purpose. It pulls lemmas based on the use of a word lexicon. But if the word is not found in the lexicon, it defines rules that turn the word into a lemma. This tool has been 97.5% accurate in its attempts to lemmatize an evaluation set prepared from the CRAFT corpus.



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