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What are the applications of Lemmatization?

Aqsa Amir


Lemmatization is a technique of grouping different inflectional forms of words together with the same root or lemma. After a morphological analysis of the word, the lemmatization process returns the word's root or the dictionary word. Using this technique, each word is reduced from its inflectional form to its root word to understand the text better.

For instance, the lemmatization process would return the root word ‘good’ for ‘good,’ ‘better,’ and ‘best.’ In addition, it would return the word ‘leaf’ for ‘leafs’ and ‘leaves.’

The difference between lemmatization and stemming

Lemmatization and stemming are often confused by people as similar processes. Lemmatization returns the root words of the inflected words by conducting a morphological analysis using a dictionary. On the other hand, stemming is a process that cuts off the start and the end of the word. It removes prefixes and suffixes and returns the remaining word. For instance, lemmatization returns the root word ‘study’ when we enter ‘studies.’ However, stemming would return ‘studi’ by only removing the suffix ‘es’ when we enter the same word.

The lemmatization process carefully returns the root word by first understanding the context in which the word has been used. However, this doesn't happen in stemming. Therefore, lemmatization is more complex and time-consuming than stemming.

The lemmatization process goes through the entire dictionary to find the correct word, making it slower. However, stemming only cuts off the prefixes and suffixes without understanding the word's context. Therefore, it is faster than lemmatization but less accurate.

One lemma can have numerous stems, and one stem can have multiple lemmas. Hence, both these processes are different from one another.

Applications of lemmatization

Lemmatization is a crucial process used extensively in Machine Learning (ML), Artificial Intelligence (AI), and Big Data analytics. It is widely used in Natural Language Processing (NLP) and Natural Language Understanding (NLU). It helps robots and machines to understand and converse with humans as accurately as possible.

Chatbots use lemmatization to understand customer queries and respond to them accordingly. In addition, lemmatization is a crucial component of text mining, where we extract similar and relevant information based on the text provided to us. A few other applications of the lemmatization process are listed below:

  • Search Engine Optimization: Lemmatization helps search engines respond to user queries in the best possible way. It retrieves the information the user is searching. It also displays and groups it with other relevant information that might be helpful for the user. The search engine lemmatizes the query to understand it better and then provides accurate results for the query. Similar documents are grouped and displayed to the user's end.
  • Sentiment Analysis: To better understand the user’s sentiments in a message, the text is lemmatized and then analyzed. Lemmatization helps group positive and negative reviews. This can be quite useful in analyzing how well a product is working in the market. It is also helpful to analyze the sentiments of the people regarding an ongoing situation.
  • Information Retrieval: Systems can easily retrieve information and group similar data using lemmatization. This enhances the performance of search engines as well as other searching algorithms.

Advantages and disadvantages of lemmatization

Many advantages and disadvantages of lemmatization might have been prominent by now. This process helps ac a better understanding of the text and provides accurate results by understanding the context in which the words are used. Lemmatization is used in numerous applications that we use daily.

However, it is a slow and time-consuming process because it uses a dictionary to conduct a morphological analysis of the inflected words. This adds a computational overhead.


To sum up, lemmatization is a process that provides the root words of the inflected words. This helps us better understand the text and helps achieve accurate results in response the users' queries.



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