NLTK

NLTK, the Natural Language Toolkit, is a comprehensive open-source Python library for natural language processing and computational linguistics, providing over fifty corpora, lexical resources, and processing modules for tasks including tokenization, stemming, lemmatization, part-of-speech tagging, parsing, semantic reasoning, and text classification, widely used in education, research, and industry for building language-aware applications. The tokenization tools split text into sentences and individual words, handling punctuation, contractions, and multi-word expressions, with customizable tokenizers for different languages and text formats including social media text, academic papers, and informal online communication. The stemming and lemmatization modules reduce words to their root forms, supporting algorithms including Porter stemmer, Snowball stemmer for multiple languages, and WordNet-based lemmatization that maps words to their dictionary lemma forms considering part-of-speech context for more accurate normalization. The part-of-speech tagger assigns grammatical categories to words using trained models, and the named entity recognizer identifies people, organizations, locations, and other entity types in text. The parsing tools include regular expression-based chunk parsers, context-free grammar parsers, and dependency parsers for analyzing sentence structure. The corpus readers provide access to standard linguistic datasets including the Brown Corpus, Reuters news corpus, WordNet semantic network, and Project Gutenberg literary texts. The classification module supports Naive Bayes, decision trees, maximum entropy models, and support vector machines for text classification tasks like sentiment analysis and topic detection. Designed for NLP researchers, students, data scientists, linguists, and developers.