[강의자료] 현재 언어처리 기술 현황과 통계적 접근, 사용하는 이유

현재 언어처리 기술 현황과 통계적 접근, 사용하는 이유 3주 강의 The Dream It’d be great if machines could Process our email (usefully) Translate languages accurately Help us manage, summarize, and aggregate information Use speech as a UI (when needed) Talk to us / listen to us But they can’t: Language is complex, ambiguous, flexible, and subtle Good solutions need linguistics and machine learning knowledge So: What is NLP? Fundamental goal: deep understand of broad language Not just string processing or keyword matching! End systems that we want to build: Ambitious: speech recognition, machine translation, information extraction, dialog interfaces, question answering, trend finding … Modest: spelling correction, text categorization… Speech Systems Automatic Speech Recognition (ASR) Audio in, text out SOTA: 0.3% for digit strings, 5% dictation, 50%+ TV Text to Speech (TTS) Text in, audio out SOTA: totally intelligible (if sometimes unnatural) Speech systems currently: Model the speech s…
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