1. 2003.09.09 - SLIDE 1IS 202 – FALL 2003 Lecture 5: Lexical Relations & WordNet Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30 am - 12:00 pm Fall 2003 http://www.sims.berkeley.edu/academics/courses/is202/f03/ SIMS 202: Information Organization and Retrieval

2. 2003.09.09 - SLIDE 2IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

3. 2003.09.09 - SLIDE 3IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

4. 2003.09.09 - SLIDE 4IS 202 – FALL 2003 Definition of AI “... artificial intelligence [AI] is the science of making machines do things that would require intelligence if done by [humans]” (Minsky, 1963)

5. 2003.09.09 - SLIDE 5IS 202 – FALL 2003 The Goals of AI Are Not New Ancient Greece –Daedalus’ automata Judaism’s myth of the Golem 18 th century automata –Singing, dancing, playing chess? Mechanical metaphors for mind –Clock –Telegraph/telephone network –Computer

6. 2003.09.09 - SLIDE 6IS 202 – FALL 2003 Some Areas of AI Knowledge representation Programming languages Natural language understanding Speech understanding Vision Robotics Planning Machine learning Expert systems Qualitative simulation

7. 2003.09.09 - SLIDE 7IS 202 – FALL 2003 AI or IA? Artificial Intelligence (AI) –Make machines as smart as (or smarter than) people Intelligence Amplification (IA) –Use machines to make people smarter

8. 2003.09.09 - SLIDE 8IS 202 – FALL 2003 Furnas: The Vocabulary Problem People use different words to describe the same things –“If one person assigns the name of an item, other untutored people will fail to access it on 80 to 90 percent of their attempts.” –“Simply stated, the data tell us there is no one good access term for most objects.”

9. 2003.09.09 - SLIDE 9IS 202 – FALL 2003 The Vocabulary Problem How is it that we come to understand each other? –Shared context –Dialogue How can machines come to understand what we say? –Shared context? –Dialogue?

10. 2003.09.09 - SLIDE 10IS 202 – FALL 2003 Vocabulary Problem Solutions? Furnas et al. –Make the user memorize precise system meanings –Have the user and system interact to identify the precise referent –Provide infinite aliases to objects Minsky and Lenat –Give the system “commonsense” so it can understand what the user’s words can mean

11. 2003.09.09 - SLIDE 11IS 202 – FALL 2003 CYC Decades long effort to build a commonsense knowledge-base Storied past 100,000 basic concepts 1,000,000 assertions about the world The validity of Cyc’s assertions are context-dependent (default reasoning)

12. 2003.09.09 - SLIDE 12IS 202 – FALL 2003 Cyc Examples Cyc can find the match between a user's query for "pictures of strong, adventurous people" and an image whose caption reads simply "a man climbing a cliff" Cyc can notice if an annual salary and an hourly salary are inadvertently being added together in a spreadsheet Cyc can combine information from multiple databases to guess which physicians in practice together had been classmates in medical school When someone searches for "Bolivia" on the Web, Cyc knows not to offer a follow-up question like "Where can I get free Bolivia online?"

13. 2003.09.09 - SLIDE 13IS 202 – FALL 2003 Cyc Applications Applications currently available or in development –Integration of Heterogeneous Databases –Knowledge-Enhanced Retrieval of Captioned Information –Guided Integration of Structured Terminology (GIST) –Distributed AI –WWW Information Retrieval Potential applications –Online brokering of goods and services –"Smart" interfaces –Intelligent character simulation for games –Enhanced virtual reality –Improved machine translation –Improved speech recognition –Sophisticated user modeling –Semantic data mining

14. 2003.09.09 - SLIDE 14IS 202 – FALL 2003 Cyc’s Top-Level Ontology Fundamentals Top Level Time and Dates Types of Predicates Spatial Relations Quantities Mathematics Contexts Groups "Doing" Transformations Changes Of State Transfer Of Possession Movement Parts of Objects Composition of Substances Agents Organizations Actors Roles Professions Emotion Propositional Attitudes Social Biology Chemistry Physiology General Medicine http://www.cyc.com/cyc-2-1/toc.html Materials Waves Devices Construction Financial Food Clothing Weather Geography Transportation Information Perception Agreements Linguistic Terms Documentation

15. 2003.09.09 - SLIDE 15IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

16. 2003.09.09 - SLIDE 16IS 202 – FALL 2003 Syntax The syntax of a language is to be understood as a set of rules which accounts for the distribution of word forms throughout the sentences of a language These rules codify permissible combinations of classes of word forms

17. 2003.09.09 - SLIDE 17IS 202 – FALL 2003 Semantics Semantics is the study of linguistic meaning Two standard approaches to lexical semantics (cf., sentential semantics; and, logical semantics): –(1) compositional –(2) relational

18. 2003.09.09 - SLIDE 18IS 202 – FALL 2003 Lexical Semantics: Compositional Approach Compositional lexical semantics, introduced by Katz & Fodor (1963), analyzes the meaning of a word in much the same way a sentence is analyzed into semantic components. The semantic components of a word are not themselves considered to be words, but are abstract elements (semantic atoms) postulated in order to describe word meanings (semantic molecules) and to explain the semantic relations between words. For example, the representation of bachelor might be ANIMATE and HUMAN and MALE and ADULT and NEVER MARRIED. The representation of man might be ANIMATE and HUMAN and MALE and ADULT; because all the semantic components of man are included in the semantic components of bachelor, it can be inferred that bachelor  man. In addition, there are implicational rules between semantic components, e.g. HUMAN  ANIMATE, which also look very much like meaning postulates. –George Miller, “On Knowing a Word,” 1999

19. 2003.09.09 - SLIDE 19IS 202 – FALL 2003 Lexical Semantics: Relational Approach Relational lexical semantics was first introduced by Carnap (1956) in the form of meaning postulates, where each postulate stated a semantic relation between words. A meaning postulate might look something like dog  animal (if x is a dog then x is an animal) or, adding logical constants, bachelor  man and never married [if x is a bachelor then x is a man and not(x has married)] or tall  not short [if x is tall then not(x is short)]. The meaning of a word was given, roughly, by the set of all meaning postulates in which it occurs. –George Miller, “On Knowing a Word,” 1999

20. 2003.09.09 - SLIDE 20IS 202 – FALL 2003 Pragmatics Deals with the relation between signs or linguistic expressions and their users Deixis (literally “pointing out”) –E.g., “I’ll be back in an hour” depends upon the time of the utterance Conversational implicature –A: “Can you tell me the time?” –B: “Well, the milkman has come.” [I don’t know exactly, but perhaps you can deduce it from some extra information I give you.] Presupposition –“Are you still such a bad driver?” Speech acts –Constatives vs. performatives –E.g., “I second the motion.” Conversational structure –E.g., turn-taking rules

21. 2003.09.09 - SLIDE 21IS 202 – FALL 2003 Language Language only hints at meaning Most meaning of text lies within our minds and common understanding –“How much is that doggy in the window?” How much: social system of barter and trade (not the size of the dog) “doggy” implies childlike, plaintive, probably cannot do the purchasing on their own “in the window” implies behind a store window, not really inside a window, requires notion of window shopping

22. 2003.09.09 - SLIDE 22IS 202 – FALL 2003 Semantics: The Meaning of Symbols Semantics versus Syntax –add(3,4) –3 + 4 –(different syntax, same meaning) Meaning versus Representation –What a person’s name is versus who they are A rose by any other name... –What the computer program “looks like” versus what it actually does

23. 2003.09.09 - SLIDE 23IS 202 – FALL 2003 Semantics Semantics: assigning meanings to symbols and expressions –Usually involves defining: Objects Properties of objects Relations between objects –More detailed versions include Events Time Places Measurements (quantities)

24. 2003.09.09 - SLIDE 24IS 202 – FALL 2003 The Role of Context The concept associated with the symbol “21” means different things in different contexts –Examples? The question “Is there any salt?” –Asked of a waiter at a restaurant –Asked of an environmental scientist at work

25. 2003.09.09 - SLIDE 25IS 202 – FALL 2003 What’s in a Sentence? “A sentence is not a verbal snapshot or movie of an event. In framing an utterance, you have to abstract away from everything you know, or can picture, about a situation, and present a schematic version which conveys the essentials. In terms of grammatical marking, there is not enough time in the speech situation for any language to allow for the marking of everything which could possibly be significant to the message.” Dan Slobin, in Language Acquisition: The state of the art, 1982

26. 2003.09.09 - SLIDE 26IS 202 – FALL 2003 Lexical Relations Conceptual relations link concepts –Goal of Artificial Intelligence Lexical relations link words –Goal of Linguistics

27. 2003.09.09 - SLIDE 27IS 202 – FALL 2003 Major Lexical Relations Synonymy Polysemy Metonymy Hyponymy/Hypernymy Meronymy/Holonymy Antonymy

28. 2003.09.09 - SLIDE 28IS 202 – FALL 2003 Synonymy Different ways of expressing related concepts Examples –cat, feline, Siamese cat Overlaps with basic and subordinate levels Synonyms are almost never truly substitutable –Used in different contexts –Have different implications This is a point of contention

29. 2003.09.09 - SLIDE 29IS 202 – FALL 2003 Polysemy Most words have more than one sense –Homonym: same sound and/or spelling, different meaning (http://www.wikipedia.org/wiki/Homonym) bank (river) bank (financial) –Polysemy: different senses of same word (http://www.wikipedia.org/wiki/Polysemy) That dog has floppy ears. She has a good ear for jazz. bank (financial) has several related senses –the building, the institution, the notion of where money is stored

30. 2003.09.09 - SLIDE 30IS 202 – FALL 2003 Metonymy Use one aspect of something to stand for the whole –The building stands for the institution of the bank. –Newscast: “The White House released new figures today.” –Waitperson: “The ham sandwich spilled his drink.”

31. 2003.09.09 - SLIDE 31IS 202 – FALL 2003 Hyponymy/Hyperonymy ISA relation Related to Superordinate and Subordinate level categories –hyponym(robin,bird) –hyponym(emu,bird) –hyponym(bird,animal) –hyperym(animal,bird) A is a hypernym of B if B is a type of A A is a hyponym of B if A is a type of B

32. 2003.09.09 - SLIDE 32IS 202 – FALL 2003 Basic-Level Categories (Review) Brown 1958, 1965, Berlin et al., 1972, 1973 Folk biology: –Unique beginner: plant, animal –Life form: tree, bush, flower –Generic name: pine, oak, maple, elm –Specific name: Ponderosa pine, white pine –Varietal name: Western Ponderosa pine No overlap between levels Level 3 is basic –Corresponds to genus –Folk biological categories correspond accurately to scientific biological categories only at the basic level

33. 2003.09.09 - SLIDE 33IS 202 – FALL 2003 Psychologically Primary Levels SUPERORDINATE animal furniture BASIC LEVEL dog chair SUBORDINATE terrier rocker Children take longer to learn superordinate Superordinate not associated with mental images or motor actions

34. 2003.09.09 - SLIDE 34IS 202 – FALL 2003 Meronymy/Holonymy Part/Whole relation –meronym(beak,bird) –meronym(bark,tree) –holonym(tree,bark) Transitive conceptually but not lexically –The knob is a part of the door. –The door is a part of the house. –? The knob is a part of the house ? Holonyms are (approximately) the inverse of meronyms

35. 2003.09.09 - SLIDE 35IS 202 – FALL 2003 Antonymy Lexical opposites –antonym(large, small) –antonym(big, small) –antonym(big, little) –but not large, little Many antonymous relations can be reliably detected by looking for statistical correlations in large text collections. (Justeson & Katz 91)

36. 2003.09.09 - SLIDE 36IS 202 – FALL 2003 Thesauri and Lexical Relations Polysemy: same word, different senses of meaning –Slightly different concepts expressed similarly Synonyms: different words, related senses of meanings –Different ways to express similar concepts Thesauri help draw all these together Thesauri also commonly define a set of relations between terms that is similar to lexical relations –BT, NT, RT More on Thesauri next week…

37. 2003.09.09 - SLIDE 37IS 202 – FALL 2003 What is an Ontology? From Merriam-Webster’s Collegiate –A branch of metaphysics concerned with the nature and relations of being –A particular theory about the nature of being or the kinds of existence More prosaically –A carving up of the world’s meanings –Determine what things exist, but not how they inter- relate Related terms –Taxonomy, dictionary, category structure Commonly used now in CS literature to describe structures that function as Thesauri

38. 2003.09.09 - SLIDE 38IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

39. 2003.09.09 - SLIDE 39IS 202 – FALL 2003 WordNet Started in 1985 by George Miller, students, and colleagues at the Cognitive Science Laboratory, Princeton University –Miller also known as the author of the paper “The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information” (1956) Can be downloaded for free: –www.cogsci.princeton.edu/~wn/

40. 2003.09.09 - SLIDE 40IS 202 – FALL 2003 Miller on WordNet “In terms of coverage, WordNet’s goals differ little from those of a good standard college-level dictionary, and the semantics of WordNet is based on the notion of word sense that lexicographers have traditionally used in writing dictionaries. It is in the organization of that information that WordNet aspires to innovation.” –(Miller, 1998, Chapter 1)

41. 2003.09.09 - SLIDE 41IS 202 – FALL 2003 Presuppositions of WordNet Project Separability hypothesis –The lexical component of language can be separated and studied in its own right Patterning hypothesis –People have knowledge of the systematic patterns and relations between word meanings Comprehensiveness hypothesis –Computational linguistics programs need a store of lexical knowledge that is as extensive as that which people have

42. 2003.09.09 - SLIDE 42IS 202 – FALL 2003 WordNet: Size POSUniqueSynsets Strings Noun 107930 74488 Verb 10806 12754 Adjective 21365 18523 Adverb 4583 3612 Totals144684 109377 WordNet Uses “Synsets” – sets of synonymous terms

43. 2003.09.09 - SLIDE 43IS 202 – FALL 2003 Structure of WordNet

44. 2003.09.09 - SLIDE 44IS 202 – FALL 2003 Structure of WordNet

45. 2003.09.09 - SLIDE 45IS 202 – FALL 2003 Structure of WordNet

46. 2003.09.09 - SLIDE 46IS 202 – FALL 2003 Unique Beginners Entity, something –(anything having existence (living or nonliving)) Psychological_feature –(a feature of the mental life of a living organism) Abstraction –(a general concept formed by extracting common features from specific examples) State –(the way something is with respect to its main attributes; "the current state of knowledge"; "his state of health"; "in a weak financial state") Event –(something that happens at a given place and time)

47. 2003.09.09 - SLIDE 47IS 202 – FALL 2003 Unique Beginners Act, human_action, human_activity –(something that people do or cause to happen) Group, grouping –(any number of entities (members) considered as a unit) Possession –(anything owned or possessed) Phenomenon –(any state or process known through the senses rather than by intuition or reasoning)

48. 2003.09.09 - SLIDE 48IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

49. 2003.09.09 - SLIDE 49IS 202 – FALL 2003 WordNet Demo Available online (from Unix) if you wish to try it… –Login to irony and type “wn word” for any word you are interested in –Demo…

50. 2003.09.09 - SLIDE 50IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

51. 2003.09.09 - SLIDE 51IS 202 – FALL 2003 Discussion Questions Joe Hall on Lexical Relations and WordNet –Which method of linguistic analysis do you think will be more fruitful... the painstaking process involved with building WordNet or the relatively easy output afforded by Church et al.'s computational method that, however, requires much work to decipher the results?

52. 2003.09.09 - SLIDE 52IS 202 – FALL 2003 Discussion Questions Joe Hall on Lexical Relations and WordNet –What are the problems/advantages of using the World Wide Web itself as a "corpus"? (If you were to incorporate the current digital copies of all newspapers, journals, etc. wouldn't you very quickly exceed the 15 Million words of the largest corpus in the Church article?)

53. 2003.09.09 - SLIDE 53IS 202 – FALL 2003 Discussion Questions Joe Hall on Lexical Relations and WordNet –With the diversity of dialects of the English language, how much does this type of computational analysis get confused by phrases such as "What up?" (i.e., slang)? Aren't these some of the more interesting parts of language (i.e., how language evolves)?

54. 2003.09.09 - SLIDE 54IS 202 – FALL 2003 Lecture Overview Review Lexical Relations WordNet Demo Discussion Questions Action Items for Next Time Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

55. 2003.09.09 - SLIDE 55IS 202 – FALL 2003 Homework Read Chapters 3 and 5 of The Organization of Information (Textbook) Discussion Question volunteers? –Tu Tran –Hong Qu

56. 2003.09.09 - SLIDE 56IS 202 – FALL 2003 Next Time Introduction to Metadata