introduction to natural language processing(NLP).ppt
- 2. What is NLP? • Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. Its primary goal is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. • A somewhat applied field
- 3. Why Study NLP? • Human language interesting & challenging • NLP offers insights into language • Language is the medium of the web • Interdisciplinary: Ling, CS, psych, math • Help in communication • With computers (ASR, TTS) • With other humans (MT) • Ambitious yet practical
- 4. Goals of NLP • Scientific Goal • Identify the computational machinery needed for an agent to exhibit various forms of linguistic behavior • Engineering Goal • Design, implement, and test systems that process natural languages for practical applications
- 5. Applications • speech processing: get flight information or book a hotel over the phone • information extraction: discover names of people and events they participate in, from a document • machine translation: translate a document from one human language into another • question answering: find answers to natural language questions in a text collection or database • summarization: generate a short biography of Noam Chomsky from one or more news articles
- 6. General Themes • Ambiguity of Language • Language as a formal system • Computation with human language • Rule-based vs. Statistical Methods • The need for efficiency
- 7. Topic Ideas 1.Text to Speech – artificial voices 2.Speech Recognition - understanding 3.Textual Analysis – readability 4.Plagiarism Detection – candidate selection 5.Intelligent Agents – machine interaction
- 8. Text to Speech – artificial voice • Text Input • Break text into phonemes • Match phonemes to voice elements • Concatenate voice elements • Manipulate pitch and spacing • Output results • Research question: How can a human voice be used to produce an artificial voice?
- 9. Speech Recognition • Spoken Input • Identify words and phonemes in speech • Generate text for recognized word parts • Concatenate text elements • Perform spelling, grammar and context checking • Output results • Research question: How can speech recognition assist a deaf student taking notes in class?
- 10. Textual Analysis - Readability • Text Input • Analyze text & estimate “readability” • Grade level of writing • Consistency of writing • Appropriateness for certain educ. level • Output results • Research question: How can computer analyze text and measure readability? • Opportunities for hands-on research
- 11. Plagiarism Detection • Text Input • Analyze text & locate “candidates” • Find one or more passages that might be plagiarized • Algorithm tries to do what a teacher does • Search on Internet for candidate matches • Output results • Research question: What algorithms work like humans when finding plagiarism? • Experimental CS research
- 12. Intelligent Agents • Example: ELIZA • AIML: Artificial Intelligence Modeling Lang. • Human types something • Computer parses, “understands”, and generates response • Response is viewed by human • Research question: How can computers “understand” and “generate” human writing? • Also good area for experimentation