Top 20 NLTK Interview Questions To Prepare For In 2023

Natural Language Toolkit (NLTK) is one of the most popular libraries for natural language processing (NLP) in Python. It provides a wide range of text processing libraries and programs that make NLP tasks easier. From tokenization to part-of-speech tagging, stemming to chunking, NLTK has useful tools for various NLP techniques.

As NLTK usage grows, knowledge of the library has become an essential skill for NLP professionals. With companies increasingly using NLP for text analytics and chatbots, NLTK skills are highly sought after in job interviews.

To help you prepare for NLTK interview questions, I have put together the top 20 questions that are commonly asked Going through these questions will boost your confidence and help you perform better in the interview.

Basics of NLTK

1. What is NLTK? What are its key features?

NLTK or Natural Language Toolkit is an open source Python library for natural language processing It contains a wide range of text processing libraries and programs for NLP tasks like

• Tokenizing and separating text into words, punctuation
• Removing stop words
• Stemming and lemmatization
• Part-of-speech tagging
• Named entity recognition
• Syntactic parsing
• Text classification

NLTK makes NLP easier by providing simple, well-documented interfaces to highly optimized text processing routines. Some of its key features are:

• A large collection of corpora, datasets, trained models
• Extensive documentation and tutorials
• Easy to use API for text processing tasks
• Support for statistical NLP and machine learning

1. How is NLTK different from spaCy and other NLP libraries?

While both NLTK and spaCy provide text processing capabilities there are some key differences

• NLTK contains a wider range of text processing libraries and algorithms compared to spaCy.
• spaCy provides a faster and more efficient implementation while NLTK provides more flexibility.
• spaCy has better support for advanced deep learning-based models while NLTK is better for traditional NLP tasks.
• NLTK supports more languages than spaCy.
• spaCy has a cleaner API while NLTK has a string processing style API.

So while spaCy is great for production use cases, NLTK provides a great learning environment to understand text processing and NLP fundamentals.

1. What are the key NLP tasks where NLTK can be used?

NLTK provides text processing capabilities that can be used across various NLP tasks:

• Tokenization: Splitting text into words, punctuations
• Stemming: Reducing words to their root form
• Lemmatization: Reducing words to lemma or dictionary form
• Stop words removal: Removing common words like ‘a’, ‘and’, ‘the’
• POS tagging: Tagging words with their parts-of-speech
• Named entity recognition (NER): Identifying entities like persons, locations organizations
• Syntactic parsing: Analyzing sentence structure
• Sentiment analysis: Detecting sentiment of text
• Text classification: Categorizing text into topics
• Language modeling: Predicting the next word in a sequence
• Machine translation: Translating text from one language to another

So NLTK is useful across most common NLP applications.

Working with NLTK

1. How do you install NLTK?

NLTK can be easily installed using pip on Python:

pip install nltk

After installation, you need to download the NLTK packages to use its resources:

import nltknltk.download() 

This opens the NLTK downloader to download corpora, models, etc. Some key packages to download are:

• stopwords
• averaged_perceptron_tagger
• punkt
• wordnet

1. Explain the NLTK corpus with examples.

A corpus is a collection of texts or documents that are used for language analysis and NLP. NLTK contains various corpora that can be used for experimentation and building models. Some examples of NLTK corpora are:

• Brown corpus: Collection of text from 500 sources
• Gutenberg corpus: Collection of books from Project Gutenberg
• Reuters corpus: Collection of 10,000 news documents
• Inaugural Address Corpus: Collection of inaugural addresses by US presidents

For example, to access the inaugural address corpus:

from nltk.corpus import inauguralinaugural.fileids() # ['1789-Washington.txt', '1793-Washington.txt', ...] inaugural.raw('1789-Washington.txt)[:100]# 'Fellow Citizens of the Senate and the House of Representatives Among the vicissitudes incident to life, no event could have filled me with greater anxieties than that of which the notification was transmitted by your order'

1. How can you tokenize sentences and words using NLTK?

NLTK provides convenient methods for tokenizing text into sentences and words:

Sentence Tokenization:

from nltk.tokenize import sent_tokenizetext = "NLTK is a useful tool for NLP tasks. It contains many corpora and modules."sent_tokenize(text)# ['NLTK is a useful tool for NLP tasks.', 'It contains many corpora and modules.'] 

Word Tokenization:

from nltk.tokenize import word_tokenizetext = "NLTK is a useful tool for NLP tasks."  word_tokenize(text)# ['NLTK', 'is', 'a', 'useful', 'tool', 'for', 'NLP', 'tasks', '.']

We can tokenize in a single line as well:

[word_tokenize(t) for t in sent_tokenize(text)] 

1. How can you perform stemming and lemmatization in NLTK?

Stemming reduces words to their root form by removing suffixes like ‘ing’, ‘ly’, ‘es’, ‘s’ etc.

from nltk.stem import PorterStemmerstemmer = PorterStemmer()stemmer.stem('working') # 'work'stemmer.stem('played') # 'play' 

Lemmatization reduces words to their lemma or dictionary form using vocabulary and morphological analysis.

from nltk.stem import WordNetLemmatizer lemmatizer = WordNetLemmatizer()lemmatizer.lemmatize('working') # 'work'lemmatizer.lemmatize('played') # 'play'

While both generate root words, lemmatization is better as it uses vocabulary context to get correct lemma whereas stemming just chops off suffixes.

1. How can you perform part-of-speech tagging in NLTK?

NLTK provides POS taggers to tag each token with its part of speech like noun, verb, adjective etc.

Example using the default NLTK POS tagger:

import nltktext = "NLTK is a useful tool for NLP tasks."nltk.pos_tag(nltk.word_tokenize(text))# [('NLTK', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('useful', 'JJ'), ('tool', 'NN'), ('for', 'IN'), ('NLP', 'NNP'), ('tasks', 'NNS'), ('.', '.')]

The Penn Treebank tagset is used for POS tagging by default in NLTK.

NLTK for Text Classification

1. How can you perform text classification using NLTK?

The NLTK NaiveBayesClassifier can be used for basic text classification as follows:

• Split data into training and test sets
• Extract features using bag-of-words, TF-IDF etc.
• Train a NaiveBayesClassifier with training set
• Use classifier to predict categories for test set
• Evaluate accuracy of classifier

For example:

import nltkfrom nltk.classify import NaiveBayesClassifier# Split to training and test setstrain_data = [(text1, 'pos'), (text2, 'neg'), ...] test_data = [(text3, 'pos'), (text4, 'neg'), ...]# Extract featurestrain_set = [(extract_features(t),c) for t,c in train_data]test_set = [(extract_features(t),c) for t,c in test_data]# Train classifier classifier = NaiveBayesClassifier.train(train_set) # Predict categoriespredictions = classifier.classify_many([fs for (fs,l) in test_set])# Evaluate accuracyprint(nltk.classify.accuracy(classifier, test_set))

We can extract various features like bag of words, TF-IDF weights, parts-of-speech tags etc. More advanced classifiers can also be