Natural Language Processing (NLP) enables computers to understand, analyze and interact with human language. It consists of multiple phases that work together to process language and extract meaningful information.
- Helps computers understand human language
- Processes both structure and meaning of text
- Uses multiple phases for language analysis
- Applied in chatbots, translation, sentiment analysis and search systems
1. Lexical and Morphological Analysis
Lexical and Morphological Analysis are the initial phases of NLP that help computers understand words, their structure and their grammatical roles.
Lexical Analysis
Lexical analysis focuses on identifying and processing words by breaking text into meaningful units called tokens.
- Tokenization splits text into smaller units called tokens
- Part-of-Speech Tagging assigns grammatical roles such as noun, verb or adjective to words
- Helps identify words for further language processing
- Simplifies text for NLP tasks
Example
Sentence: "I am reading a book."
- Tokenization: ["I", "am", "reading", "a", "book"]
- Part-of-Speech Tagging: ["I" → Pronoun (PRP), "am" → Verb (VBP), "reading" → Verb (VBG), "a" → Article (DT), "book" → Noun (NN)]
Morphological Analysis
Morphological analysis studies the internal structure of words using morphemes, the smallest units of meaning.
- Stemming reduces words to their root form like "running" to "run".
- Lemmatization converts words to their base or dictionary form considering the context like "better" becomes "good".
- Helps understand word formation and structure
- Improves accuracy in parsing, translation and tagging tasks
- Supports advanced NLP applications through word normalization
2. Syntactic Analysis (Parsing)
Syntactic Analysis, also called parsing, helps understand how words are arranged in a sentence according to grammar rules. It identifies relationships between words and creates a parse tree representing the sentence structure.
- Analyzes grammatical structure of sentences
- Identifies components such as subject, verb and object
- Creates parse trees to represent sentence structure
- Helps machines understand relationships between words
Key components
- POS Tagging: Assigns grammatical categories such as noun, verb, and adjective
- Ambiguity Resolution: Handles words with multiple meanings based on context
Example
Consider the following sentences
- Correct Syntax: "John eats an apple."
- Incorrect Syntax: "Apple eats John an."
Although both sentences contain the same words, only the first sentence follows correct grammatical structure and conveys meaningful information.
3. Semantic Analysis
Semantic Analysis focuses on understanding the meaning of words and sentences. It ensures that text is not only grammatically correct but also logically meaningful and contextually appropriate.
- Understands the meaning of words and sentences
- Checks logical and contextual correctness of text
- Interprets words based on surrounding context
- Improves language understanding in NLP systems
Key Tasks
- Named Entity Recognition (NER): Identifies entities such as people, locations, organizations and dates
- Word Sense Disambiguation (WSD): Determines the correct meaning of words with multiple meanings based on context
Example
Sentence: “Apple eats a John.”
Although the sentence is grammatically correct, it is semantically incorrect because an apple cannot perform the action of eating a person.
4. Discourse Integration
Discourse Integration focuses on understanding how multiple sentences connect within a larger context. It ensures that the meaning of a text remains coherent and consistent across sentences and paragraphs.
- Connects related sentences and ideas
- Maintains contextual understanding across text
- Helps interpret references and meanings correctly
- Improves coherence in long or complex texts
Key Aspects
- Anaphora Resolution: Identifies references such as pronouns and links them to the correct subject
- Contextual References: Understands words or phrases based on surrounding context
Example
- “Taylor went to the store. She bought groceries.” : “She” refers to “Taylor”
- “This is unfair!” : The meaning of “this” depends on the surrounding context
5. Pragmatic Analysis
Pragmatic Analysis focuses on understanding the intended meaning of words and sentences by considering context, tone, and speaker intention beyond the literal meaning.
- Understands implied meanings and intentions
- Interprets context, tone and conversational meaning
- Identifies figurative language such as idioms and metaphors
- Helps machines understand human communication naturally
- Supports sentiment analysis, chatbots, and conversational AI
- Improves context-aware and human like responses
Key Tasks
- Understanding Intentions: Identifies the actual purpose behind statements or questions
- Figurative Meaning: Interprets non literal expressions such as idioms and metaphors
Examples
- “Can you pass the salt?” : A polite request, not a question about ability
- “I’m falling for you.” : Expresses love, not literal falling
- “Hello! What time is it?” : May indicate concern about being late depending on context