Natural Language Processing: Syntax, Semantics, and Key Techniques

Rahul S
2 min readAug 26, 2023

In the realm of Natural Language Processing (NLP), understanding the nuances of language is essential for tasks ranging from language generation to sentiment analysis. This foundation rests upon two pillars: Syntax and Semantics. Together, they provide the framework for comprehending and generating human language.

SYNTAX

Syntax is the structural backbone of language, encompassing the grammatical rules that govern how words, phrases, and clauses are arranged to construct valid sentences.

It ensures that a sentence conforms to the correct order and usage of linguistic elements within a specific language. A syntactically correct sentence adheres to these grammatical rules, ensuring that its structural elements align correctly.

However, for a sentence to be semantically correct, it must not only adhere to syntax but also convey a meaningful message. This requires that the constituent words and their arrangement make sense in the real-world context.

SYNTACTIC ANALYSIS

Syntactic Analysis is the process of examining text to discern and enforce the grammatical rules specific to a particular language. This process often involves techniques such as parsing, which deconstructs sentences into their component parts.

PARSING: Parsing generates a parse tree, a hierarchical structure that represents the syntactic relationships among words in a sentence. This tree aids in the identification of subjects, verbs, objects, and other grammatical elements, facilitating an understanding of sentence structure.

SEMANTIC ANALYSIS

Semantic Analysis, on the other hand, delves beyond syntax to unravel the meaning embedded within language. It seeks to interpret the real-world context and implications of words, phrases, and sentences. Semantic analysis strives to grasp the deeper layers of meaning by considering how linguistic constructs relate to one another and their contextual significance.

STEMMING: Stemming is a critical technique in NLP that focuses on simplifying language. It involves reducing words to their stems, which are the core components that remain after removing prefixes and…

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