NLTK (Natural Language Toolkit) is a popular Python library used for building natural language processing (NLP) applications. It provides easy‑to‑use tools for text preprocessing, linguistic analysis and basic machine learning tasks in NLP.
Installing
Learn how to install NLTK across different platforms.
Basics
This section introduces the basic tools to manipulate and analyze text data efficiently.
Text Preprocessing Techniques
Preprocessing steps for NLP includes removing stopwords and punctuation, adding custom stopwords and applying stemming and lemmatization.
- Removing stop words
- Remove punctuations
- Custom Stopwords and Remove Them
- Stemming words
- Lemmatization
- Accessing Text Corpora and Lexical Resources
- Complete Text Preprocessing
Tokenization
Learn how to split text and audio streams into meaningful units using customized strategies.
Feature Extraction
This section focuses on transforming text into structured data for machine learning applications.
- N-Gram Language Modelling
- Part of Speech Tagging
- Named Entity Recognition
- Measure similarity
- WordNet for tagging
- Topic Modeling
- How to use CoreNLPParser
- Location Tags Extraction
Text Analysis
These tools help uncover insights and patterns in textual data
- Find Likely Word Tags
- Frequency of words
- Generate bigrams
- Text Classification
- Semantic Analysis
- Dependency Parsing
Advance Techniques
This section explores more complex and customizable NLP operations.
- Chunking and chinking
- Classifier-based Chunking
- Named Entity Chunker Training
- Keyphrase Extraction
- Synonyms and Antonyms
- Custom corpus
- Wordlist Corpus
- Snowball Stemmer
- Synsets for a word in WordNet
- Backoff Tagging to combine taggers
- Training Unigram Tagger
Projects
These projects provide practical experience using NLTK for real-world NLP tasks.