Answer
NLTK for tokenization and parsing.
spaCy for fast NLP pipelines.
Text preprocessing for ML.
spaCy for fast NLP pipelines.
Text preprocessing for ML.
Quick Answer
Python NLP tools: NLTK (classical NLP - tokenization, stemming, POS tagging, good for learning). spaCy (production NLP - fast, pre-trained models, named entity recognition). Hugging Face Transformers (BERT, GPT models - state of the art). Gensim (word embeddings - Word2Vec). TextBlob (simple sentiment analysis). Use spaCy for production, Transformers for deep learning NLP tasks.
S
SugharaIQ Editorial Team
Verified Answer
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Source: SugharaIQ