NLTK Assignments — Process. Analyze. Understand.

The Natural Language Toolkit (NLTK) is one of the most widely used Python libraries for Natural Language Processing (NLP). These assignments are designed to build your expertise from text preprocessing all the way to language modeling and applications.

Each assignment contains 20 Basic, 20 Intermediate, and 10 Advanced questions to ensure that you practice progressively — from simple tokenization to advanced semantic and statistical language models.

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Why Practice with These Assignments?

  • Build strong foundations in text processing, tokenization, stemming, and lemmatization.
  • Work with standard corpora and lexical resources like WordNet, Brown, Gutenberg, and Reuters.
  • Gain hands-on experience in POS tagging, parsing, and chunking.
  • Explore text classification and semantic analysis.
  • Learn language modeling techniques and apply them in real-world NLP applications.
  • Strengthen your data preprocessing pipeline for Machine Learning and Deep Learning projects.

How It Works

  • Attempt each assignment in sequence for smooth progression.
  • Run the code in Jupyter Notebook, VS Code, or PyCharm.
  • Use NLTK’s built-in datasets (tips, Reuters, Brown, Gutenberg, WordNet, etc.).
  • Save your outputs (tables, plots, trees) for review.
  • Maintain a log of concepts learned after every task.

What You’ll Achieve

  • Develop a complete NLP toolkit from scratch.
  • Understand both linguistic theory and applied text processing.
  • Gain hands-on experience in building classifiers, parsers, and semantic analyzers.
  • Apply statistical and semantic language models for real-world problems.
  • Be prepared for Data Science, Machine Learning, and NLP-focused job roles.

Browse the Assignments

  • Assignment 1 — Text Preprocessing
    Learn fundamental NLP operations: tokenization, case-folding, stopword removal, stemming, lemmatization, regex-based tokenization, and text-cleaning pipelines. Build robust preprocessing functions for social text, HTML, Unicode, and emojis.
  • Assignment 2 — Corpora, Lexical Resources & Data Handling
    Work with NLTK’s corpora (Gutenberg, Brown, Reuters, Inaugural). Learn frequency distributions, KWIC concordances, collocations, and lexical diversity. Explore WordNet synsets, hypernyms, hyponyms, antonyms, and semantic similarity.
  • Assignment 3 — POS Tagging & Morphological Analysis
    Understand Penn Treebank and Universal POS tagsets. Practice POS tagging, frequency distributions, ambiguity analysis, and backoff models (Unigram, Bigram, Trigram, Regexp, Affix). Train and evaluate custom taggers with error analysis.
  • Assignment 4 — Parsing & Syntax Trees
    Learn to design Context-Free Grammars (CFGs). Use parsers like ChartParser and EarleyChartParser. Perform chunking, chinking, and Named Entity Recognition (NER). Build parse trees and analyze syntactic ambiguities.
  • Assignment 5 — Text Classification
    Train classifiers on the movie_reviews and Reuters corpora. Build Naive Bayes, Decision Trees, and MaxEnt models. Explore feature extraction (BoW, bigrams, trigrams, TF-IDF, chi-square, MI). Evaluate using accuracy, precision, recall, F1, and confusion matrices.
  • Assignment 6 — Semantic Analysis
    Work with Word Sense Disambiguation (WSD) using the Lesk algorithm. Explore WordNet similarities, synonyms, antonyms, hypernyms, paraphrasing, and query expansion. Learn semantic role labeling (SRL) heuristics and build semantic retrieval applications.
  • Assignment 7 — Language Modeling & Applications
    Learn n-gram models (unigram, bigram, trigram) using NLTK and Brown corpus. Implement MLE, Laplace, Lidstone, and interpolated smoothing. Build collocation finders, perplexity calculators, and chatbot intent classifiers. Apply language models to toy datasets and retrieval tasks.

Tips for Success

  • Start small: always test tokenization and preprocessing before complex models.
  • Explore corpora variety — don’t just stick to one dataset.
  • Compare different models and feature extraction methods.
  • Save and visualize results (trees, FreqDist, collocation lists).
  • Work with Indian names and contexts where possible to keep examples relatable.

Ready to build real confidence in NLTK? Pick a set below and start solving!

Assignment-1

Assignment-2

Assignment-3

Assignment-4

Assignment-5

Assignment-6

Assignment-7

FAQs

Q1. Do I need to download corpora separately?
Yes. NLTK requires nltk.download() for resources like WordNet, Brown, Gutenberg, and Reuters.

Q2. Are these assignments beginner-friendly?
Yes. Assignment 1 starts with preprocessing basics, and later assignments build toward advanced NLP.

Q3. How much time should I allocate per assignment?
On average 1–2 hours depending on complexity and familiarity with NLP.

Q4. Are these assignments suitable for Machine Learning projects?
Absolutely. Preprocessing, classification, and language modeling skills are essential for ML and DL pipelines.

Q5. What programming knowledge do I need?
Basic knowledge of Python, regex, and dictionaries is sufficient to start.

Q6. Do these assignments include semantic and contextual analysis?
Yes. Assignments 6 and 7 cover semantic analysis, WSD, query expansion, and language modeling.

Q7. Can I apply these concepts in research projects?
Yes. NLTK is widely used in research, academia, and NLP prototyping.

Q8. Do these assignments include evaluation techniques?
Yes. Assignments 3, 5, and 7 focus heavily on accuracy, confusion matrices, and error analysis.

Q9. Is there any integration with external libraries?
Yes. Some tasks suggest scikit-learn for TF-IDF, χ2 square, and logistic regression.

Q10. What’s the end goal?
By the end, you’ll have the skills to preprocess, analyze, classify, parse, and model natural language using Python’s most popular NLP library.