Comparing Python’s Stemming with Lemmatization

Python’s stemming and lemmatization functionalities are implemented to condense text down to its most rudimentary form. These techniques are employed by search engines and conversational interfaces to understand the intent behind user queries. Although stemming focuses solely on word roots or search queries themselves, both stemming and lemmatization in Python consider the context of the search query.

While looking for information, it is advantageous to receive search results that pertain not only to the exact search query, but also to relevant variations of the keyword. This guarantees that users obtain an all-encompassing list of results that is pertinent to their query.

Normalisation techniques, such as lemmatization and stemming, are employed in Natural Language Processing (NLP) to ensure consistency in texts. Both approaches work by breaking down words into their fundamental components.

In this tutorial, we will furnish code snippets and examples to illustrate the implementation of stemming and lemmatization. By employing the Natural Language Toolkit (NLTK) library in Python, we will obtain a more in-depth understanding of these two methods.

The Lemmatization Function of Python

The process of lemmatization involves converting a search query or term to its dictionary form or lemma. This approach examines only the segment of the word that can be dissected into its basic components. As a result, we can determine the core lemma, or root form, of the term or query. This technique is particularly valuable for precision and efficiency when poring over vast amounts of textual data.

Let’s explore some of the applications of lemmatization.

  1. Indexing Systems

    Lemmatization is an extensively used technique by search engines and chatbots to grasp the significance and purpose behind user queries. Many search engines are the major implementers of this approach.
  2. Condensed Cataloguing

    Index values are employed to store information efficiently.
  3. Biomedicine

    The analysis of lemmatization is beneficial for the biomedicine field. It enhances the efficiency of data retrieval and related procedures.

Built on the Foundation of the Python Language

Stemming is a technique that entails the use of rules to determine the root of a word and find its stem. By indiscriminately removing word endings, this approach streamlines the process. The implementation of Python stemming, furthermore, enhances this process by normalising sentence length and consolidating search results, thereby enhancing the legibility of the text.


Stemming a word excessively could lead to its meaning becoming distorted, and the resultant stem word could be meaningless. This occurrence thus entails words with the identical root assuming multiple meanings. For instance, in this specific context, words could be reduced to their root form, such as “univers”.

  • University
  • Universal
  • Universe

In this context, even though each of these three terms have the same etymological root, their meanings could not be any more disparate.


In this given example, two different concepts are expressed by using words that have a shared root. The problem of under-stemming arises when a text involves terms that are equivalent or interchangeable with each other.

The word “alumnus” can serve as a good example. To see the different possibilities, we can examine:

  • Alumni
  • Alumna
  • Alumnae

Due to the Latin morphology of this word, these antonyms cannot be substituted for each other.

Distinguishing Stems from Lemmas

Stemming and lemmatization are two techniques used to simplify complicated words by reducing them to a single, uniform form. Though both processes share the same fundamental concept, they differ significantly in their approaches and outcomes. Stemming involves reducing words to their basic form by eliminating affixes such as prefixes and suffixes. Conversely, lemmatization involves reducing words to their dictionary form while considering their morphological structure. Thus, lemmatization produces more precise outcomes than stemming.

The stemming process entails the exclusion of the first or last letters of the original word. Additionally, it takes into account which prefixes are more commonly employed.

In contrast, Python’s lemmatization process is centred on exploring a word’s etymology to establish its root lemma, which is responsible for all its inflected forms.

Both lemmatization and stemming are operations that prepare a word for machine translation.

Classifying data entails having vectorization as a handy tool. Vectorization enables array operations without the use of “for” loops. This implies that the current data being handled can be converted into something entirely distinct with the assistance of a vectorizer.

Using stemming or lemmatization methods makes it feasible to decrease the pool of words employed in a given corpus. This minimization of the lexicon results in less ambiguity while training a model, resulting in better model output performance. Therefore, utilising these techniques can significantly increase the model’s efficiency.

In the following sections, we will offer a complete guide to the Python Natural Language Toolkit (NLTK) and how it can be employed to perform stemming and lemmatization in your projects. Additionally, we will discuss the merits and drawbacks of both techniques and provide instructions on how to implement them in your project.

Different Types of Word Roots

As previously mentioned, stemming is a rule-based text normalization technique that eliminates the prefix and suffix of a word to attain its root form. When compared to lemmatization, which considers the word’s context, stemming is a quicker procedure. However, stemming’s aggressive nature may yield inaccurate outcomes in a dataset.

Porter Stemming Algorithm

The Porter Stemming Algorithm, also known as the Porter Stemmer, is a suffix-stemming method for generating word stems. To gain a better comprehension of the Porter Stemming Algorithm, we can examine the subsequent code example that employs the nltk library to construct a stemmer object.

Snowball Stemmer

The Snowball stemmer method, also called the Porter2 stemmer, is an upgraded edition of the Porter stemmer algorithm that overcomes a few of its shortcomings.

Lancaster Word Roots

The Lancaster stemming technique is highly effective because of its stringent methodology of over-stemming several phrases, which involves reducing each term to its most basic root. This is a more severe technique than Porter stemming and Porter2 stemming, making it a desirable option when creating language-based software.

Different Types of Lemmatization

In Python, lemmatization is a technique that starts by determining the part of speech (POS) of a word, as opposed to the stemming approach which only truncates a word to its root. This technique considers the word’s context and converts it to its lemmatized form, maintaining the original meaning of the word.

WordNet Lemmatizer

WordNet, the lexical database, is extensively utilised by leading search engines and is frequently applied in information retrieval research. It is a widely recognised lemmatizer offering various lemmatization functionalities.

After examining the above code snippet, it is apparent that “plays” and “pharmacies” have been transformed to their base forms while all other words remain unchanged. Typically, a lemmatizer presumes all words to be nouns unless otherwise indicated. To avoid this, the lemmatizer must be given the corresponding part of speech (POS) tag for the word.

It is worth noting that when the word “better” is given a part-of-speech tag, a distinct result is obtained. The tag can distinguish between “better” as an adjective and as an adverb, acknowledging both usages.

For the lemmatizer to determine the correct Part-of-Speech (POS) tag for each word, it must perform additional analysis. Nonetheless, supplying a POS marker for each word in a large volume of texts can be a challenging endeavour.

Multiple lemmatizers are accessible for application on a specific vocabulary.

  • Our preferred lemmatizer is TextBlob

  • CoreNLP of San Francisco

  • Comprehensive Lemmatizer

  • Gensim Lemmatizer

Of all the lemmatizer techniques, only the Spacy approach cannot be used in conjunction with provided POS tags.

The base form of any word can be produced through the use of either lemmatization or stemming techniques. The key distinction between the two is the shape of the resulting stem. Unlike stemming which can lead to ambiguous results, lemmatization provides a more precise and meaningful output.

While lemmatization in Python may be slower than stemming, it can improve the training speed of a machine learning model. The Snowball stemmer, which employs the Porter2 technique, is particularly effective with large datasets. If the machine learning model you’re employing is based on a count vectorizer that doesn’t consider the context of the words or texts analyzed, then stemming is the recommended approach.


  1. Which is better: lemmatization or stemming?

    Stemming is easier and quicker to install. However, there may be errors, and these errors might not be significant in some scenarios.

    Lemmatization, on the other hand, provides more precise results by considering the words’ part-of-speech and generating actual words. Lemmatization is more complex and time-consuming than stemming.

    If you value the accuracy of your results, lemmatization is the superior option. This approach has a negligible impact on modern systems’ performance. On the other hand, if you prioritize performance, stemming algorithms are the optimal choice.
  2. I’m unsure whether I should use lemmatization or stemming.

    Regardless of the method, the text inputs necessitate transformation into numerical vectors. Term Frequency Inverse Document Frequency (TF-IDF) is a useful approach for achieving this. This method compares a word’s frequency to its overall corpus prevalence, resulting in an effective means of quantifying words’ relative importance within a document.

    If the text is lengthy but utilizes a restricted vocabulary, stemming can provide benefits. When the text is brief but utilises an extensive vocabulary, word embeddings may be appropriate. However, considering the resource cost, the benefit-to-cost ratio of employing lemmatization is relatively low and may not be worthwhile.

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