Exploring Natural Language Understanding: Computational Models and Applications

1. 991015主要工作 • Review the last lecture • 991008補課問題 • 課本第一章 國立政治大學資訊科學系

2. Chapter 1 Introduction to Natural Language Understanding

3. Who’s Who in this Field • Linguists • Psycholinguists • Philosophers • Computational linguists • Computer scientists • AI: Artificial Intelligence • Business persons 國立政治大學資訊科學系

4. Computational Models • Motivation for developing computational models of language understanding • Scientific • Understand human perception • A test bed for theories about language understanding • Cognitive Science • Practical/Technological • What if computers understand natural languages? • Enhance what computers can do for human 國立政治大學資訊科學系

5. Applications of NLP • Text-based and dialogue-based (one-way and two-way communication between computers and their users) • Text-based applications • Find relevant documents • Find relevant information in documents • Machine translation • Summarize documents 國立政治大學資訊科學系

6. Position of the Book • A middle ground between the scientific and technological goals • Goal: describe work for producing linguistically motivated computational models of language understanding… • Recommended background knowledge: linguistics, AI, and logic 國立政治大學資訊科學系

7. Dialogue-Based Applications • Q&A systems (telephone operators) • Tutoring systems • Speech-controlled systems • Problem-solving systems • Whatever you can imagine! 國立政治大學資訊科學系

8. Keyword Matching • A traditional way • A common method used in computer systems in many libraries and web cites • Not NLP however! • Difference: Understanding! “… it must compute some representation of the information hat can be used for later inference.” 國立政治大學資訊科學系

9. Dialogue-based Applications • Challenge: need to maintain natural, smooth-flowing dialogue • “A speech recognition system need not involve any language understanding.” • Author’s viewpoint on page 5-6 國立政治大學資訊科學系

10. What is Understanding? • What does this word mean? • Does a compiler program understand the high level programming language that it translates? • Does a calculator understand arithmetic? • Does Deep Blue really understand chess? • A crucial characteristic of an understanding system must compute some representation of the information that can be used for later inference. (page 4) 國立政治大學資訊科學系

11. Evaluation Methods • Black box evaluation • Does a system perform very well in BBE really understand? • Glass box evaluation • Check the system components 國立政治大學資訊科學系

12. ELIZA • Figure 1.2 on page 7 • How does ELIZA work? • Would you change your evaluation of ELIZA once you learn how it works? • Now the algorithm … 國立政治大學資訊科學系

13. Algorithm of ELIZA • Data • A database of keywords • Each keyword associated with • a number (priority) • a pattern to match and • a specification of the output • Procedure • Find keywords with matching patterns • Select the keyword with the highest priority if there are multiple candidate keywords. • Generate innocuous continuation statement if no candidate keywords. • Prevent unnatural repetition 國立政治大學資訊科學系

14. Does ELIZA Understand? • Is it a collection of tricks? • Why do people might think ELIZA does understand? • “… ELIZA appears to be intelligent because you use your own intelligence to make sense of what it says.” • Illusion of intelligence • ELIZA never say something concrete. 國立政治大學資訊科學系

15. Off-the-Wall Responses • Ex1: • H: Necessity is the mother of invention. • E: Tell me more about your family. • Ex2: • H: Green the adzabak are the a ran four. • E: What if they were not the a ran four. • ELIZA does not retain any of the content in the conversation. 國立政治大學資訊科學系

16. Relevant Information for NLP • (Page 10) Linguistics and world knowledge • Phonetic and phonological • Morphological • Syntactic • Semantic • Pragmatic • Discourse • World (common sense) 國立政治大學資訊科學系

17. Syntax, Semantics, and Pragmatics • Consider each example as a candidate for the initial sentence of this book: • Language is one of the fundamental aspects of human behavior and is a crucial component of our lives. • Green frogs have large noses. • Green ideas have large noses. • Large have green ideas nose. • Pragmatically well-formed but not syntactically well-formed!!! (page 11) 國立政治大學資訊科學系

18. Representation and Understanding • Why not use the sentence itself as a representation of its meaning? • A word can have multiple senses. • Cook, dish, still • Ambiguity • Formal languages • Useful representation languages must: • Be precise and unambiguous • Capture the intuitive structure of the sentences… 國立政治大學資訊科學系

19. Sentence Structures John sold the book to Mary. The book was sold to Mary by John. • Are these two sentences exactly the same? • Answer to the question “What did John do for Mary?” • After it fell in the river… • Context matters! • Asterisk (*) for ill-formed sentences… 國立政治大學資訊科學系

20. Syntax • Syntactically ill-formed sentences in NLP *John are in the corner. *John put the book. • Robustness preferred? • Flying planes are dangerous. • Flying plans is dangerous. • Context-free grammar • Parse trees in Figure 1.4 國立政治大學資訊科學系

21. The Logic Form • Structures alone do not reflect meaning of sentences. • Meaning context-dependent • Logic form: representation of context-independent meaning of sentences • Semantic roles of phrases included • Disambiguation 國立政治大學資訊科學系

22. Final Representation • general knowledge representation used to represent and reason • This book adopts first-order predicate calculus (FOPC). 國立政治大學資訊科學系

23. Organization of NLP systems • Figure 1.5 on page 16 • Combining components for syntactic and semantic processing • Bidirectional grammar 國立政治大學資訊科學系