Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, generate, and respond to human language.
- It combines linguistics, machine learning, and deep learning to process text and speech in a meaningful way.
Importance of NLP:
- Helps computers interpret and analyze human language in spoken or written form.
- Enables chatbots, virtual assistants, and translation services like Google Translate and Siri.
- Used in sentiment analysis, text summarization, and speech recognition.
Two Main Components of NLP:
1.) Natural Language Understanding (NLU):
NLU focuses on enabling computers to comprehend human language by analyzing grammar, meaning, and context.
Functions:
- Lexical Analysis: Understanding word meanings and structure.
- Syntax Analysis: Checking the grammatical structure of sentences.
- Semantic Analysis: Understanding the meaning of words in context.
Example:
- NLU helps AI understand customer queries in chatbots.
- It is used in voice assistants like Alexa to understand commands.
2.) Natural Language Generation (NLG):
NLG enables computers to generate human-like text responses after processing information.
Functions:
- Content Planning: Deciding what information to include.
- Text Structuring: Arranging words logically.
- Sentence Generation: Converting structured data into natural language.
Example:
- NLG is used in news automation, where AI generates news summaries.
- It powers AI writing tools like ChatGPT.
Steps of Natural Language Processing (NLP):
Natural Language Processing (NLP) involves multiple steps that allow computers to understand, process, and generate human language. These steps help convert raw text into structured, meaningful data that AI systems can analyze and use.
1.) Lexical Analysis (Segmentation, Morphological Analysis):
Lexical analysis involves breaking a sentence into words or tokens and analyzing their structure.
key components:
- Tokenization: Splits text into words, phrases, or meaningful elements.
- Morphological Analysis: Identifies the root form of words (e.g., “running” → “run”).
- Removes unnecessary elements like punctuation and special characters.
Example:
Sentence: “AI is transforming industries.”
- Tokens: [“AI”, “is”, “transforming”, “industries”]
- Morphological Analysis:
- “Transforming” → Root word: “Transform”
- “Industries” → Singular form: “Industry”
2.) Syntactic Analysis (Parsing):
Syntactic analysis checks the grammatical structure of a sentence to ensure it follows language rules.
Features:
- Identifies subject, verb, and object relationships.
- Uses grammar rules and parsing algorithms to structure sentences.
- Detects incorrect grammar and sentence structures.
Example:
- Correct Sentence: “She eats an apple.” ✅
- Incorrect Sentence: “Eats she apple an.” ❌ (Wrong syntax)
3.) Semantic Analysis:
Semantic analysis focuses on understanding the meaning of words and sentences.
Features:
- Identifies word meanings and relationships.
- Differentiates words with multiple meanings based on context.
- Uses synonyms, antonyms, and named entity recognition (NER).
Example:
“Apple” could refer to:
- A fruit 🍏
- A tech company (Apple Inc.) 💻
Semantic analysis determines the correct meaning based on the sentence.
4.) Pragmatic Analysis:
Pragmatic analysis interprets sentences based on context, real-world knowledge, and intent.
Features:
- Detects sarcasm, humor, or implied meanings.
- Understands user intent in conversational AI.
- Ensures proper responses in chatbots and voice assistants.
Example:
- “Can you pass the salt?” → A request, not just a question.
- “I love waiting in long lines.” → Likely sarcastic.
5.) Machine Translation:
Machine translation converts text from one language to another using AI models.
Features:
- Uses linguistic rules and deep learning for translation.
- Translates entire sentences while maintaining context and meaning.
Example:
- English: “How are you?”
- Spanish: “¿Cómo estás?”
- Used in Google Translate, DeepL, and language learning apps.
6.) Speech Recognition (Speech-to-Text Conversion):
Speech recognition converts spoken language into text.
Features:
- Uses audio signal processing to detect words.
- Powers voice assistants like Siri, Alexa, and Google Assistant.
Example:
- Spoken Input: “Set an alarm for 7 AM.”
- Converted Text: [“Set”, “an”, “alarm”, “for”, “7”, “AM”]
