English Lemmatization: Simplifying Words in NLP

Language, in all its complexity, offers multiple ways to express similar concepts. We have “running”, “ran”, and “runner” — all derivatives of the simple verb “run”. In Natural Language Processing (NLP), navigating these variations efficiently is crucial. Enter lemmatization, a technique that helps simplify and standardize words. Let’s dive into the concept of English lemmatization and its indispensable role in NLP.

What is Lemmatization?

Lemmatization is the process of reducing a word to its base or root form. It involves understanding the context and meaning of a word to transform it into its most generic version, called a ‘lemma’.

In English, the base form for a verb is the simple infinitive. For example, the gerund “striking” and the past form “struck” are both forms of the lemma “(to) strike”.
The base form for a noun is the singular form. For example, the plural “mice” is a form of the lemma “mouse.”

For instance:

“Running” becomes “run”
“Mice” becomes “mouse”
“Better” might become “good”

Contrary to simple stemming, which often chops off prefixes or suffixes without understanding the context (turning “running” into “runn”), lemmatization ensures the root word (or lemma) is a valid word in the language.

Why is Lemmatization Important in NLP?

  • Data Reduction: Textual data is vast and diverse. Lemmatization helps condense it by treating different forms of a word as a single entity. This reduction is beneficial for storage, processing speed, and consistency.
  • Standardization: NLP tasks like text classification, sentiment analysis, or topic modeling require standardized input. Lemmatization ensures that words with similar meanings, despite their surface variations, are treated uniformly.
  • Improved Search: For search algorithms, “running”, “runs”, and “ran” should ideally yield similar results. Lemmatization aids in achieving this by ensuring all these variants match the root, “run”.
  • Better Text Analysis: For tasks like document clustering or topic modeling, lemmatization can result in more accurate groupings and analyses, as the noise introduced by word form variations is minimized.

How Does Lemmatization Work?

Lemmatization isn’t a straightforward process of rule-based truncation. Instead, it:

  • Analyzes the word’s meaning in its sentence context.
  • Uses detailed dictionaries or morphological analysis techniques.
  • Considers part of speech, as many words can be both nouns and verbs (e.g., “display”).

Lemmatization is part of NLP (Natural language processing) can do.
Before doing a lemmatization is necessary to separate the sentence into tokens. A lemmatizer takes a token and its part-of-speech tag as input and returns the word’s lemma.

This process, called tokenization, segments strings into smaller parts.
In this post, we show different ways of tokenization the OpenNLP Tokenizer API provides.
Furthermore, the Word Frequency tool separates words into tokens and shows their part of speech.

Challenges with Lemmatization

While lemmatization is powerful, it’s not without challenges:

  • Computational Overheads: Compared to stemming, lemmatization is computationally more intensive due to its reliance on dictionaries and morphological analysis.
  • Contextual Ambiguities: Words with multiple meanings or parts of speech might not always be lemmatized correctly without sufficient context.


Lemmatization, with its ability to distill words to their essence, plays a pivotal role in many NLP tasks. It aids in data reduction, improves algorithmic efficiency, and brings consistency to textual data. Whether you’re building a recommendation engine, chatbot, or sentiment analyzer, incorporating lemmatization can significantly enhance your system’s understanding of language nuances.