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CSTalks-Natural Language Processing-17Aug
This document provides an overview of natural language processing (NLP). It begins with examples of NLP applications like translation and question answering. It then discusses the backgrounds in artificial intelligence, linguistics, and the web. The document outlines several common NLP tasks like part-of-speech tagging, named-entity recognition, word sense disambiguation, and parsing. It also discusses challenges like ambiguity in natural language. The document concludes with a discussion of why NLP is difficult due to ambiguity at both the linguistic and acoustic levels.
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CSTalks-Natural Language Processing-17Aug
- 1. Natural Language Processing Daniel Dahlmeier NUS Graduate School for Integrative Sciences and Engineering [email protected] CSTalks 2 November 2011
- 2. Acknowledgments Examplesand figures from Michael Collins’ lecture notes: http://www.cs.columbia.edu/∼mcollins. Some other figures are from Wikipedia: http://www.wikipedia.org. The rest I randomly found on the web.
- 3. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Google translate 3/25
- 4. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion IBM’s Watson computer wins at Jeopardy! 4/25
- 5. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Siri 5/25
- 6. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion What is Natural Language Processing? Natural Language Processing (NLP) or Computational Linguistics Language processing that goes beyond a “bag of words” representation. Example Translate from one language into the other. Answer natural language questions. Parse the syntactic/semantic structure of a sentence. The other NLP NLP = neuro-linguistic programming. 6/25
- 7. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Background(s): Artificial Intelligence Talk to your computer Dave: Hello, HAL. Do you read me, HAL? HAL: Affirmative, Dave. I read you. Dave: Open the pod bay doors, HAL. HAL: I’m sorry, Dave. I’m afraid I can’t do that. The computer needs to ... Understand the user : Natural Language Understanding. Generate a well-formed reply : Natural Language Generation. 7/25
- 8. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Background(s): Artificial Intelligence (cont.) Turing Test Experimenter talks to two parties A and B via a terminal. If C cannot distinguish which party is a computer and which is a human, we should consider the computer to be intelligent. Natural language is deeply intertwined with intelligence. 8/25
- 9. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Background(s): Linguistics Generative Linguistics Humans can produce and understand an infinite number of sentences by means of a finite set of rules. Language is produced through a generative, recursive process in the human brain. The principles that underlie this process are universal to all languages (universal grammar). 9/25
- 10. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Background(s): the Web “We are drowning in information but starved for knowledge.” by Edward Osborne Wilson Too much text to read... Wikipedia: over 3.7 million articles (English). PubMed: over 20 million citations. WWW: billions of pages, trillions of words. 10/25
- 11. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Part-of-speech Tagging Part-of-speech tagging Input: a sentence. Output: a part-of-speech tag sequence, e.g., noun, verb, adjective,... Example Profits/N soared/V at/P Boeing/N Co./N ,/, easily/ADV topping/V forecasts/N on/P Wall/N Street/N ./. 11/25
- 12. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Named-entity recognition Named-entity recognition Input: a sentence. Output: a BIO-named entity tag sequence, e.g., PERSON, ORGANIZATION, OTHER. Example Profits/O soared/O at/O Boeing/B-ORG Co./I-ORG ,/O easily/O topping/O forecasts/O on/O Wall/O Street/O ./O 12/25
- 13. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Word Sense Disambiguation Word sense disambiguation Input: a sentence. Output: the sense of each word in the sentence. Example I/sense1 can/sense1 can/sense2 a/sense1 can sense3 . 13/25
- 14. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Parsing Parsing Input: a sentence. Output: the syntactic tree structure of the sentence. Example Boeing is located in Seattle. 14/25
- 15. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Machine translation Machine Translation Input: a sentence in language F . Output: the translated sentence in language E . Example Input: Syriens Pr¨sident Baschar al-Assad hat den Westen davor a gewarnt, sich in die Angelegenheiten seines Landes einzumischen. Output: Syrian President Bashar al-Assad has warned the West against interfering in the affairs of his country. 15/25
- 16. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Why is it hard? ( example from L.Lee) “At last, a computer that understands you like your mother” 16/25
- 17. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Ambiguity of Natural Language “At last, a computer that understands you like your mother” This could mean... 1 It understands you as well as your mother understands you. 2 It understands (that) you like your mother. 3 It understands you as well as it understands your mother. 1 and 3: Does this mean well, or poorly? 17/25
- 18. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Ambiguity at the Acoustic Level “At last, a computer that understands you like your mother” This sounds like... 1 “... a computer that understands you like your mother.” 2 “... a computer that understands you lie cured mother.” 18/25
- 19. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Ambiguity at the Syntactic (structure) Level “At last, a computer that understands you like your mother” 19/25
- 20. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Ambiguity at the Syntactic (structure) Level “List all flights on Tuesday.” 20/25
- 21. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Ambiguity at the Semantic (meaning) Level Definition of “mother” 1 a woman who has given birth to a child 2 a stringy slimy substance consisting of yeast cells and bacteria; is added to cider or wine to produce vinegar. More ambiguity They put money in the bank (= buried in mud?). I saw her duck with a telescope (= a duck carrying a telescope?). 21/25
- 22. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Ambiguity at the Discourse (multi-clause) Level Anaphora resolution Alice says they’ve built a computer that understands you like your mother. But she ... ... doesn’t know any details (Alice) ... doesn’t understand me at all (my mother) 22/25
- 23. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Related Stuff Machine Learning This really made large-scale, open domain NLP applications possible. Information Retrieval Both need to “understand” language. Linguistics Interested in the nature of language. Psychology / Cognitive Science Both interested in human cognitive capabilities. 23/25
- 24. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Conclusion What I have told you... What NLP is about. Some NLP tasks that people work on. Why it’s not that easy. What I haven’t told you How do you solve all these problems? How well does it work? What is left to be done? 24/25
- 25. Examples What is NLP? Background NLP tasks Why is it hard? Related Stuff Conclusion Would you like to know more? NLP courses at NUS CS4248: natural language processing CS6207: advanced natural language processing Books Jurafsky and Martin, Speech and Language Processing (2nd Edition) 25/25