NLP (Natural Language Processing)
Natural Language Processing (NLP) is a field of artificial intelligence that enables machines to understand, interpret, and generate human language. It combines computational linguistics with machine learning and deep learning to analyze texts, extract meaning, and respond intelligently. It is applied in tasks such as: Tokenization and lemmatization (dividing text and reducing words to their base form). Sentiment analysis (determining emotions in a text). Entity recognition (identifying names of people, places, dates, etc.). Machine translation (Google Translate, DeepL). Chatbots and virtual assistants (Alexa, Siri).
spaCy performs entity recognition, dependency parsing, tokenization, etc.
NLTK (Natural Language Toolkit)- a classic Python library for text processing that supports tokenization, stemming, lemmatization, and POS tagging.
Stanza (Stanford NLP)- supports over 60 languages and provides pre-trained models for morphological analysis, POS tagging, and more.
Hugging Face Transformers- supports models like BERT, GPT, T5, XLNet, etc. It is optimized for NLP with deep learning and designed for tasks such as translation, text summarization, and chatbots.
Gensim- specializes in topic modeling and similarity analysis. It uses models like Word2Vec, FastText, LDA, etc.