Master NLP with NLTK in Python

An overview of the purpose and importance of preprocessing text in NLP tasks.

Breaks down text into sentences and words using NLTK's tokenization tools.

Demonstrates how to filter out common stopwords to clean and focus textual data.

Introduces stemming techniques to reduce words to their root forms using algorithms like Porter Stemmer.

Explains how lemmatization refines word normalization by considering context and grammar.

Combines tokenization, stopwords removal, stemming, and lemmatization into a complete preprocessing pipeline.

Combines tokenization, stopwords removal, stemming, and lemmatization into a complete preprocessing pipeline.

Common mistakes to avoid while performing preprocessing

Introduces the concept of a corpus in NLP and the different types available in NLTK.

Exploring and Analyzing the GutenBerg Corpus

Exploring and Analyzing the Reuters Corpus

Exploring and Analyzing the Brown Corpus

Teaches how to calculate and interpret word frequency distributions.

Demonstrates tools for finding word context and usage patterns within corpora.

Bringing your own corpus from outside into NLTK for analysis

Learners build a tool to compare writing styles of different authors using word frequencies and sentence structures.

Explains what part-of-speech tagging is and why it's fundamental for grammatical analysis.

Demonstrates how to tag words with their grammatical roles using NLTK.

Walks through Penn Treebank tags and how to interpret them.

Shows how to use tagged corpora for analysis and understanding usage patterns.

Introduces the concept of chunking as a way to extract useful phrases from text.

Introduces classification in NLP, including common tasks like spam detection.

Explains how to convert text into feature vectors using word counts.

Teaches how to do feature extraction for training a simple classifier using labeled data.

Teaches how to train and test a simple classifier using labeled data.

Covers accuracy metrics, confusion matrix, and model performance interpretation.

Explores how to tweak inputs and features to improve classification results.

Defines language models and how they predict the next word based on context.

Explains unigrams, bigrams, trigrams, and their use in modeling local word context.

Shows how to build and analyze a statistical language model.

Demonstrates how to generate new sentences using n-gram predictions.

Learners build a text generator trained on literary styles.

Implements a next-word suggestion tool using bigrams.

Explains how to extract structured entities like names and places from unstructured text.

Demonstrates how to use ne_chunk to tag and label named entities.

Shows how to draw and interpret parse trees for named entities.

Covers how to programmatically extract and categorize entities from parse trees.

Introduces IE and its applications like resume parsing and structured data extraction.

Covers basic regex syntax and how it applies to NLP.

Shows how to extract emails, phone numbers, and dates from raw text.

Uses chunking grammar to extract patterns like names or noun phrases.

Introduces WordNet as a lexical database and shows its value in semantic analysis.

Covers synset definitions, example usage, and how to retrieve word meanings.

Shows how to find synonyms and antonyms using WordNet’s lemma structure.

Explores word relationships like type-of and part-of.

Demonstrates how to compute semantic distance between words.

Explains polysemy and how to resolve it using the Lesk algorithm.

Builds a tool that replaces words with context-appropriate synonyms to rewrite sentences.