Computational Linguistics is the scientific study of language from a computational perspective, aimed at designing systems capable of processing human language. It incorporates elements of computer science, AI and linguistics, ultimately enabling computers to understand, generate, and interact using human language.


Think of Computational Linguistics like teaching a computer to understand and use language just like we do. It’s like helping a computer understand what we mean when we say “Hello” or answer a question like “Which is the biggest ocean?”.

In-depth explanation

Computational Linguistics is a field located at the intersection of computer science and linguistics. It is the science of creating machines that can understand, generate, and respond using human language. The ultimate objective is to design systems that can perform tasks with language, ranging from creating comprehensible sentences to understanding sentences to translating between languages.

One crucial aspect of computational linguistics is Natural Language Processing (NLP), which is the technology used to aid computers in understanding human’s natural language. It allows machines to read and understand the languages humans speak, allowing you to interact with computers using normal sentences.

Computational linguistics incorporates a variety of techniques. Parsing, for instance, involves breaking down a sentence into its grammatical components to assist a system in understanding it. Semantics, on the other hand, is the study of meaning—how we combine words to create meaning and how we use context to interpret individual words.

Machine Translation (MT) is a significant application area for computational linguistics, often using statistical or neural models to translate text between languages. Question Answering (QA), Information Extraction (IE), and Sentiment Analysis (SA) are other sub-areas, all aimed at enabling machines to understand and respond to human language more accurately and naturally.

However, it’s not easy to train computers to understand languages like humans do. Human languages are complex, with rules, exceptions to the rules, idioms, and phrases, and they evolve over time. But with advances in machine learning and deep learning, machines are getting better at these tasks every day.

Natural Language Processing (NLP), Machine Translation (MT), Sentiment Analysis (SA), Information Extraction (IE), Parsing, Semantics