Notes on Natural Language Processing (NLP)

Introduction to NLP

NLP is a branch of artificial intelligence (AI).
It enables machines to read, understand, and derive meaning from human languages.
Combines linguistics and computer science to decipher language structures.
Allows for human-like responses by machines, creating a more intelligent interaction.

NLP is more common than most realize; examples include:
- Smart Assistants: Respond to queries in a conversational manner.
- Autocorrect: Helps with spelling inaccuracies.
- Plagiarism Checkers: Analyze text for copyrighted material.

Document Processing: Start with an article or document.
Segmentation: Break down the document into sentences (punctuation based).
Tokenization: Split sentences further into individual words (tokens).
- Non-essential words (stop words like "are," "the") can be removed to streamline the process.
Stemming: Identify and reduce words to their base forms (e.g., skipping, skips, skipped).
Lemmatization: Identify base words for different forms (tense, mood, gender).
Part of Speech Tagging: Classify words into their grammatical roles (nouns, verbs, etc.).
Named Entity Tagging: Flag cultural references, names of people, movies, locations, etc.

Machine learning algorithms (e.g., Naive Bayes) are used to teach models human sentiment and speech patterns.
Many NLP techniques are based on fundamental grammar rules learned in school.

Question posed: Which NLP technique is used to obtain words from sentences?
- Options:
  a) Stemming
  b) Tokenization
  c) Lemmatization
  d) Segmentation
Participation encouraged in the comments for a chance to win Amazon gift vouchers.

Growing demand for automated language solutions leads to increased need for NLP experts.
Companies offering lucrative salaries for skilled professionals in NLP.

Suggested program: SimplyLearn's postgraduate program in AI and machine learning in collaboration with IBM.
Focus on frameworks like Keras and TensorFlow for hands-on experience in deep learning.