Lemmatization

What is lemmatization?

Lemmatization is a technique used in linguistics and natural language processing (NLP) to reduce words to their root meanings, or lemma, to identify similarities. For example, “running” can be reduced to its lemma of “run.” 

Lemmatization groups together inflected forms of the same word while considering its grammatical context, which helps tools like search engines and chatbots effectively analyze content and communicate with end users successfully.

Natural language understanding (NLU) software helps users better understand text through machine learning algorithms with outputs such as lemmatization. Some example use cases include chatbots, social media monitoring tools, and translations. By incorporating lemmatization outputs into their workflows, businesses can improve the precision of their text-based applications. 

Examples of lemmatization

As lemmatization reduces words to their lemma or base forms, inflected forms of adjectives, nouns, and verbs are transformed into their dictionary form. Below are some examples of lemmatized words in the English language: 

• “Jumping” and “jumps” would be reduced to the lemma “jump.”
• “Children” would be reduced to the lemma “child.” 
• “Cats” would be reduced to the lemma “cat.” 
• “Mapping,” “maps,” and “mapped” would be reduced to the lemma “map.”
• “Swimming” and “swims” would be reduced to the lemma “swim.”
• “Speaking,” “spoke,” and “speaks” would be reduced to the lemma “speak.”

Applications of lemmatization

Lemmatization primarily aims to normalize words by reducing them to their lemmas (or root forms). There are many different applications of lemmatization across text analysis tasks, including: 

• Search engines: Search engines use lemmatization to provide relevant search results to their users. When someone enters a search query in a search engine, the engine quickly and automatically lemmatizes the words to understand the question and return relevant results. This means that users can query any inflection of a word and receive relevant content back. 

• Sentiment analysis: Lemmatization improves the accuracy of classifying varying sentiments and making them more interpretable. This is valuable for ensuring that similar views and opinions expressed differently, such as variable verb tenses and inflections, are treated the same way during sentiment analysis modeling. 

• Language processing for assistive tools and technologies: Lemmatization enhances the support that assistive tools like chatbots and virtual assistants can provide when processing user queries. These tools use lemmatization to break down queries and respond appropriately based on the root words. 

Benefits of lemmatization

Lemmatization offers several advantages in natural language processing models. The key benefits of lemmatization include the following:

• More accurate information retrieval: Lemmatization improves the accuracy of information retrieval, particularly in systems like search engines. With a better understanding of the query based on the lemma, search engines provide the best results possible to users, thus improving the user experience. 

• Normalization of words: Lemmatization helps normalize text to reduce its variations and randomness, which helps strengthen the categorization and understanding of inflected forms of the same word. For example, “study,” “studying,” and “studies” are all reduced to “study.” The root words are actual, meaningful words from the dictionary, which differs from stemming, in which the result may not be an actual word.

• Noise reduction in text: Noisy text refers to text with differences between the surface representation of the text and the original or intended version. Noisy data includes additional meaningless information, and lemmatization helps reduce noise in text data. Since lemmatization simplifies words to their root form, it is easier to focus on the most critical content. 

Challenges of lemmatization

While lemmatization offers several advantages in natural language processing, it has disadvantages and limitations. When leveraging lemmatization, some drawbacks to consider are: 

• Potential inaccuracies: Lemmatization algorithms may not always produce accurate results. Words with irregular inflections and complex grammatical rules can impact lemma determination and produce an error, thus affecting the interpretation and output. 

• Time-consuming and slow process: Since lemmatization algorithms use morphological analysis, it can be slower than other text preprocessing techniques, such as stemming. The speed difference between text preprocessing techniques can make lemmatization less preferable depending on the application and desired output.

• Computational intensity: Lemmatization can be computationally intensive, requiring part-of-speech tagging models and dictionaries. While computational intensity is not inherently negative, it may not suit every application or scenario. 

Lemmatization vs. stemming

Lemmatization and stemming are text normalization techniques used in natural language processing, but they have distinct differences worth noting. 

Lemmatization reduces words to their base form, or lemma, to treat various word inflections consistently. Stemming is a rule-based process of reducing a word to its stem by removing prefixes or suffixes, depending on the word. 

Stemming is a faster process than lemmatization, however, lemmatization is more accurate than stemming. In some cases, the lemmatized and stemmed forms may be the same. For example, the lemma for “running” is “run,” and the stemmed version is also “run.” 

Learn more about how lemmatization and stemming fit into the broader context of natural language processing (NLP).