Text-based interactive approach: This falls under the production approach, where text written by a human is used to generate an algorithmic response. Production approach: The production approach uses a CL algorithm to produce text, which can be broken down into text-based or speech-based interactive approaches. Structural approach: This approach is more theoretical, and runs large samples of a language through CL models to better understand underlying structures of the language. Algorithms are programmed to adopt a statistical approach that does not involve grammar. Here are some key approaches you’ll want to know:ĭevelopmental approach: Like a child learning a language over time, the developmental approach simulates a similar language acquisition strategy. Since its inception in the 1950s, computational linguistics has gone through several iterations. Grammarly is an example of sentiment analysis. Sentiment analysis: This is a type of NLP that identifies emotional tone in text or spoken language. Natural language interface: These types of tools allow humans to interact with our devices’ operating systems using spoken words. An example is Wikipedia, which is the product of random editors, and can be used to train an open information extractor’s precision and recall. Knowledge extraction: Creating knowledge from unstructured and structured text sources. Many companies, such as Amazon and Verizon, have live chat available alongside phone and email options. Google Translate is a good example.Ĭhatbots: Software programs that simulate human conversation via spoken or written language, usually for customer service purposes. Machine translation: Using AI to translate from one language to another, such as from Chinese to English. There are many applications of CL in the real world. These days, it supports customer service, such as making or refunding a purchase online with a chatbot or finding information quickly with the help of Siri on iPhones. Computational linguistics first emerged to translate languages using computers, such as Chinese to English. This is important because today humans are using technology to develop tools for completing tasks more efficiently. Data scientists often analyze large amounts of written text in unstructured formats to build artifacts that can process or produce language. In practice, it requires expert knowledge of machine learning to program software that can not only understand humans but respond meaningfully. Why is computational linguistics important?Ĭomputational linguistics as a concept may seem complex. When a computer can understand language, whether written or spoken, this helps facilitate our interaction with software and machines and enables progress in fields such as customer service, research, AI tools, and much more. The field combines linguistics, computer science, artificial intelligence (AI), engineering, neuroscience, and even anthropology, to understand language from a computational perspective. Here’s what you need to know about computational linguistics and how to become a computational linguist.Ĭomputational linguistics is an interdisciplinary field that applies computer science (algorithms) to analyzing and comprehending written and spoken language. It is often linked with natural language processing (NLP), which is a subset of CL. Have you ever wondered how Alexa can listen and respond to you? Or how a customer service chatbot knows how to respond to your requests? That’s computational linguistics at work.Ĭomputational linguistics (CL) is what powers anything in a machine or device that has to do with language-speaking, writing, reading, and listening.
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