NLTK Python Library Cheatsheet

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. The Natural Language Toolkit (NLTK) is a powerful library in Python that provides tools and resources for working with human language data. Whether you’re a seasoned NLP practitioner or a beginner eager to delve into the world of language processing, this NLTK cheatsheet will be your go-to reference.

Installing NLTK

Before you start exploring the vast capabilities of NLTK, you need to install it. Open your Python environment and use the following commands:

pip install nltk

Now that NLTK is installed, let’s dive into the essential functionalities it offers.

1. Importing NLTK

import nltk

This simple line is your gateway to a plethora of NLP tools and resources.

2. Tokenization

Tokenization is the process of breaking text into words or sentences. NLTK provides powerful tokenization methods.

Word Tokenization

from nltk.tokenize import word_tokenize

text = "NLTK is an amazing toolkit for natural language processing."
tokens = word_tokenize(text)

Sentence Tokenization

from nltk.tokenize import sent_tokenize

text = "NLTK is an amazing toolkit. It makes natural language processing tasks easier."
sentences = sent_tokenize(text)

3. Stop Words Removal

Stop words are common words that do not carry much meaning. NLTK helps remove them from your text.

from nltk.corpus import stopwords

stop_words = set(stopwords.words("english"))
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]

4. Stemming and Lemmatization

Stemming reduces words to their root form, while lemmatization transforms words to their base or dictionary form.


from nltk.stem import PorterStemmer

stemmer = PorterStemmer()
stemmed_tokens = [stemmer.stem(word) for word in tokens]


from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()
lemmatized_tokens = [lemmatizer.lemmatize(word) for word in tokens]

5. Part-of-Speech Tagging

Identify the grammatical parts of each word in a sentence.

from nltk import pos_tag

pos_tags = pos_tag(tokens)

6. Named Entity Recognition (NER)

NER identifies entities like names, locations, and organizations in text.

from nltk import ne_chunk

ner_result = ne_chunk(pos_tags)

7. Frequency Distribution

Analyze the frequency of words in a text.

from nltk import FreqDist

fdist = FreqDist(tokens)

8. Concordance

Find occurrences of a word along with its context.

from nltk.text import Text

text_object = Text(tokens)

9. Word Clouds

Visualize word frequency using word clouds.

from wordcloud import WordCloud
import matplotlib.pyplot as plt

wordcloud = WordCloud().generate_from_frequencies(fdist)
plt.imshow(wordcloud, interpolation="bilinear")

10. Similarity and Distance Measurement

Compute similarity between texts using various distance metrics.

from nltk.metrics import jaccard_distance
from nltk import ngrams

text1 = "NLTK is a powerful toolkit."
text2 = "Natural Language Processing is made easier with NLTK."

ngram_distance = jaccard_distance(set(ngrams(text1, 2)), set(ngrams(text2, 2)))
print("Jaccard N-gram Distance:", ngram_distance)

This cheatsheet provides a glimpse into the versatile capabilities of NLTK. Whether you’re exploring the basics or tackling complex NLP tasks, NLTK remains an indispensable tool in your Python arsenal. As you continue your journey in natural language processing, refer back to this cheatsheet to streamline your workflow and unlock the full potential of NLTK.


1. What is NLTK, and why is it important for Natural Language Processing?

NLTK, or the Natural Language Toolkit, is a powerful Python library designed to work with human language data. It provides tools and resources for various natural language processing tasks, such as tokenization, part-of-speech tagging, and named entity recognition. NLTK is important for NLP because it simplifies complex language processing tasks, making it accessible for both beginners and experienced practitioners.

2. How can NLTK be used for text processing tasks like tokenization and stemming?

NLTK offers functions like word_tokenize for breaking text into words and PorterStemmer for stemming, which involves reducing words to their root form. These tools help in tasks such as identifying individual words in a sentence (tokenization) and reducing words to their base form (stemming), aiding in various text processing applications.

3. What is the significance of stop words, and how does NLTK handle their removal?

Stop words are common words (e.g., “the,” “is,” “and”) that often don’t contribute much meaning to a text. NLTK provides a set of stop words for multiple languages, allowing users to filter them out from their text data. The removal of stop words is crucial in improving the efficiency of text analysis by focusing on more meaningful words.

4. How does NLTK support named entity recognition (NER), and why is it important?

NLTK facilitates named entity recognition (NER) through functions like ne_chunk and part-of-speech tagging. NER identifies entities such as names, locations, and organizations within a text. This is essential for extracting key information from large datasets, enabling applications like information retrieval, sentiment analysis, and knowledge graph construction.

5. Can NLTK be used for analyzing the frequency distribution of words in a text?

Yes, NLTK provides the FreqDist class, which is used to analyze the frequency distribution of words in a given text. It helps identify the most common words and can be a valuable tool for tasks like keyword extraction, sentiment analysis, and overall text summarization. The frequency distribution is a fundamental step in understanding the characteristics of a text corpus.