Результатов для запроса ''computational linguistics': 8

Computational linguistics is the study of computer modeling of human language. This scientific field develops computational models for linguistic patterns and structures. In today's world, applications of computational linguistics fall into two categories: using computers to explain linguistic phenomena and developing systems for computers to process language. Computational linguists work to make computers understand both written and spoken language, and they produce technologies that allow humans and computers to communicate with each other linguistically using speech or language systems like artificial intelligence, text-to-speech translators, and automated voice responses.‎

Computational linguistics is a worthwhile field of study because we use computers increasingly in our modern world, and computers that understand and use human language can help us make the best use of technology. Because language is such a versatile means of communication, computers that can communicate using written or spoken language can become more effective. If you're interested in helping make interactions with machines easier, you'll enjoy the challenges of computational linguistics, and as technology continues to advance, you can develop newer and more sophisticated ways for computers to communicate.‎

When you're interested in a career in computational linguistics, you'll find plenty of opportunities for work as a computational linguist or engineer. You can find jobs helping program the next generation of artificial intelligence, or you can work as a language engineer or machine learning engineer. Your computational linguistics skills can come in handy developing apps that aid in research, web searches, or translation. You might even be able to program video games that make better use of human speech or help someone who can't speak learn to communicate.‎

Online courses on Coursera offer you opportunities to learn computational linguistics concepts and skills that are relevant to the real world, such as the philosophies behind language learning and how to program a computer to understand more than one human language or dialect. You might also learn about TensorFlow for NLP to discover the semantic similarities in texts, natural language processing with vector spaces, and sequence models in AI.‎