Natural Language Processing (NLP): Libraries, Functions, Methods, and Techniques

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Below is a comprehensive list of libraries, functions, methods, and techniques commonly used in text and natural language processing (NLP):

I. Libraries

1. NLTK (Natural Language Toolkit):

• nltk.word_tokenize(): Tokenizes a text into words.
• nltk.sent_tokenize(): Tokenizes a text into sentences.
• nltk.pos_tag(): Tags parts of speech in a sentence.
• nltk.FreqDist(): Computes the frequency distribution of words.

2. SpaCy:

• Tokenization: doc = nlp(text), where doc is a processed text.
• Part-of-speech tagging: token.pos_ provides the part of speech of a token.
• Named Entity Recognition (NER): ent.text provides the named entity.

3. TextBlob:

• TextBlob(text): Creates a TextBlob object for text processing.
• blob.words, blob.sentences: Accesses words and sentences in a TextBlob.
• blob.noun_phrases: Extracts noun phrases.
• blob.tags: Tags parts of speech.

4. Gensim: