Lemmatization is a text pre-processing technique used in natural language processing (NLP) models to break a word down to its root meaning to identify similarities.

In lemmatization, rather than just removing the suffix and the prefix, the process tries to find out the root word with its proper meaning.
Example: ‘Bricks’ becomes ‘brick,’ ‘corpora’ becomes ‘corpus,’ etc.
Let’s implement lemmatization with the help of some nltk packages.
First, we will import the required packages.

from nltk.stem import wordnet
from nltk.stem import WordnetLemmatizer

Creating an object for WordnetLemmatizer()

lemma= WordnetLemmatizer()
list = [“Dogs”, “Corpora”, “Studies”]
for n in list:
print(n + “:” + lemma.lemmatize(n))

Output:

Dogs: Dog
Corpora: Corpus
Studies: Study

Uses Of Lemmatization

emmatization is a crucial text processing technique in Natural Language Processing (NLP) that involves reducing words to their base or root form. This process helps in understanding the core meaning of the words, which is essential for various NLP tasks. Here are some of the key uses of lemmatization:

1. Improving Text Normalization:
   • Lemmatization helps in normalizing words to their base forms. For instance, words like “running,” “ran,” and “runs” are reduced to their lemma “run.” This normalization is vital for consistent text analysis.
2. Enhancing Search Engine Accuracy:
   • Search engines use lemmatization to improve search accuracy. By reducing words to their base forms, search engines can match different forms of a word to the same root, ensuring more comprehensive search results.
3. Text Mining and Information Retrieval:
   • In text mining and information retrieval, lemmatization helps in identifying relevant documents. It ensures that different forms of a word are treated as the same term, improving the accuracy of document retrieval.
4. Improving Machine Learning Models:
   • Lemmatization helps in reducing the dimensionality of the feature space by treating different forms of a word as a single feature. This reduction in dimensionality can improve the performance of machine learning models by focusing on the essential features.
5. Sentiment Analysis:
   • Lemmatization assists in sentiment analysis by normalizing words to their base forms. This normalization ensures that different forms of a word contribute consistently to the sentiment score.
6. Named Entity Recognition (NER):
   • Lemmatization helps in NER tasks by reducing the variations of words, making it easier to identify entities such as names, locations, and organizations in different forms.
7. Part-of-Speech Tagging:
   • Lemmatization supports part-of-speech tagging by providing the base form of words, which is crucial for accurate tagging and syntactic analysis.
8. Text Summarization:
   • In text summarization, lemmatization helps in generating concise summaries by focusing on the base forms of words, which can aid in creating more coherent and relevant summaries.
9. Topic Modeling:
   • Lemmatization aids in topic modeling by reducing words to their root forms, allowing for more meaningful clustering of terms and identification of topics within a text corpus.
10. Improving Translation and Cross-Language Retrieval:
    • Lemmatization enhances translation and cross-language retrieval by normalizing words, ensuring that the base meanings are preserved across different languages and forms.