Computational Linguistics is an interdisciplinary field that blends the study of human language with the capabilities of computers. It aims to understand and replicate language understanding and generation in a computational context.
Imagine you’re trying to teach your robot friend to understand and speak your language. Computational Linguistics is like the rule book you use to teach them. It includes everything from understanding the words and grammar to learning how to create meaningful sentences on their own.
Computational Linguistics (CL) is an area of study within artificial intelligence that works on automating the handling of human language. This interdisciplinary field merges concepts from linguistics, computer science, and mathematics to model or replicate human language processing with machines. The ultimate objective is to create systems that can communicate with people in a natural, human-like way, allowing machines to understand, generate, and respond in human languages.
Human languages are complex, ambiguous, innovative, and highly dependent on context. So the challenge of Computational Linguistics is to capture these characteristics in its models. This field has evolved through different methodologies, from symbolic and rule-based systems to statistical and probabilistic models, and more recently, to deep learning models.
Several subfields in CL focus on specific problems such as:
Natural Language Processing (NLP): Manipulating human language to extract meaning, generate text, or translate between languages.
Information Extraction (IE): Extracting structured information and facts from unstructured text data.
Machine Translation (MT): Translating text from one human language to another.
Speech Recognition and Generation: Transforming spoken language into written and vice versa.
Sentiment Analysis: Determining subjective information like opinions, sentiments, and emotions from the text.
Applications of computational linguistics are too manyfold: search engines, voice-operated software, customer service chatbots, machine translation services, and virtual personal assistants like Apple’s Siri, Amazon’s Alexa, and Google Assistant.
The most modern approach within Computational Linguistics is brought by Deep Learning, where language models like BERT, GPT, and transformers are tackling tasks in NLP which was thought impossible just a few years ago.
While the progress is substantial, Computational Linguistics is far from fully solving the language conundrum. The ambiguities, nuances, and infinite generative nature of human languages pose great challenges still today. Understanding humor, sarcasm, figurative speech, cultural references, or the ability to handle common-sense reasoning signifies the frontier in this field.
Natural Language Processing, Information Extraction, Machine Translation, Speech Recognition, Speech Generation, Sentiment Analysis