[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 signal
Model language
Machine Translation
....

[ppt/pdf][강의자료] 현재 언어처리 기술 현황과 통계적 접근, 사용하는 이유