1. In Defense of Contextual Vocabulary Acquisition: How to Do Things with Words in Context William J. Rapaport Department of Computer Science & Engineering, Department of Philosophy, and Center for Cognitive Science State University of New York at Buffalo Buffalo, NY 14260 http://www.cse.buffalo.edu/~rapaport/CVA/

2. “The meaning of things lies not in themselves but in our attitudes toward them.” − Antoine de Saint-Exupéry, Wisdom of the Sands (1948)

3. “The meaning of things lies not in themselves but in our attitudes toward them.” words

4. Terminology: Meaning of “Meaning” “the meaning of a word” vs. “a meaning for a word” –“the”  single meaning –“of ”  meaning belongs to word –“a”  many possible meanings depending on textual context, reader’s prior knowledge, etc. –“for”  reader constructs meaning, & gives it to word

5. Contextual Vocabulary Acquisition CVA = active, deliberate acquisition of a meaning for a word in a text by reasoning from “context” “context” ≠ textual context –surrounding words; “co-text” “context” = wide context = –“internalized” co-text … ≈ reader’s interpretive mental model of textual “co-text” –… “integrated” via belief revision … infer new beliefs from internalized co-text + prior knowledge remove inconsistent beliefs –… with reader’s prior knowledge including language knowledge including previous hypotheses about word’s meaning –but not including external sources (dictionary, humans) –  “context” for CVA is in reader’s mind, not in the text

6. Overview CVA project: 1.computational theory of how to figure out (compute) a meaning for an unfamiliar word from “wide context”. 2.convert algorithms to a teachable curriculum Current status: –Have theory –Have computational implementation Know that people do “incidental” CVA Possibly best explanation of how we learn vocabulary –given # of words high-school grad knows (~45K), & # of years to learn them (~18) = ~2.5K words/year –but only taught ~10% in 12 school years 2 groups of researchers say CVA can’t be done (well) This talk: Why they’re wrong.

7. B-R Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization P5 inference T2 I(T2) P6 T3 I(T3) P7 Note: All “contextual” reasoning is done in this “context”:

8. What does ‘brachet’ mean?

9. What Does ‘Brachet’ Mean? (From Malory’s Morte D’Arthur [page # in brackets]) 1. There came a white hart running into the hall with a white brachet next to him, and thirty couples of black hounds came running after them. [66] 2.As the hart went by the sideboard, the white brachet bit him. [66] 3.The knight arose, took up the brachet and rode away with the brachet. [66] 4.A lady came in and cried aloud to King Arthur, “Sire, the brachet is mine”. [66] 10.There was the white brachet which bayed at him fast. [72] 18. The hart lay dead; a brachet was biting on his throat, and other hounds came behind. [86]

10. Computational CVA Based on Karen Ehrlich’s CS Ph.D. dissertation (1995) Implemented in SNePS KRRA system KB: SNePS representation of reader’s prior knowledge I/P: SNePS representation of word & co-text Processing: –Inferences drawn/belief revision during text input Simulates reading –N & V definition algorithms deductively search this “belief-revised, integrated” KB (the context) for definitional information O/P: Definition frame –slots (features):classes, structure, actions, properties, etc. –fillers (values):info gleaned from context (= integrated KB)

11. Cassie learns what “brachet” means: Background info about:harts, animals, King Arthur, etc. No info about:brachets Input:formal-language (SNePS) version of simplified English A hart runs into King Arthur’s hall. In the story, B12 is a hart. In the story, B13 is a hall. In the story, B13 is King Arthur’s. In the story, B12 runs into B13. A white brachet is next to the hart. In the story, B14 is a brachet. In the story, B14 has the property “white”. Therefore, brachets are physical objects. (deduced while reading; Cassie believes that only physical objects have color)

12. --> (defineNoun "brachet") Definition of brachet: Class Inclusions: phys obj, Possible Properties: white, Possibly Similar Items: animal, mammal, deer, horse, pony, dog, I.e., a brachet is a physical object that can be white and that might be like an animal, mammal, deer, horse, pony, or dog

13. A hart runs into King Arthur’s hall. A white brachet is next to the hart. The brachet bites the hart’s buttock. --> (defineNoun "brachet") Definition of brachet: Class Inclusions: animal, Possible Actions: bite buttock, Possible Properties: white, Possibly Similar Items: mammal, pony,

14. A hart runs into King Arthur’s hall. A white brachet is next to the hart. The brachet bites the hart’s buttock. The knight picks up the brachet. The knight carries the brachet. --> (defineNoun "brachet") Definition of brachet: Class Inclusions: animal, Possible Actions: bite buttock, Possible Properties: small, white, Possibly Similar Items: mammal, pony,

15. A hart runs into King Arthur’s hall. A white brachet is next to the hart. The brachet bites the hart’s buttock. The knight picks up the brachet. The knight carries the brachet. The lady says that she wants the brachet. --> (defineNoun "brachet") Definition of brachet: Class Inclusions: animal, Possible Actions: bite buttock, Possible Properties: valuable, small, white, Possibly Similar Items: mammal, pony,

16. A hart runs into King Arthur’s hall. A white brachet is next to the hart. The brachet bites the hart’s buttock. The knight picks up the brachet. The knight carries the brachet. The lady says that she wants the brachet. The brachet bays at Sir Tor. [background knowledge: only hunting dogs bay] --> (defineNoun "brachet") Definition of brachet: Class Inclusions: hound, dog, Possible Actions: bite buttock, bay, hunt, Possible Properties: valuable, small, white, I.e. A brachet is a hound (a kind of dog) that can bite, bay, and hunt, and that may be valuable, small, and white.

17. General Comments System’s behavior  human protocols System’s definition  OED’s definition: = A brachet is “a kind of hound which hunts by scent”

18. How Does Our System Work? Uses a semantic network computer system –semantic networks = “concept maps” –serves as a model of the reader –represents: reader’s prior knowledge the text being read –can reason about the text and the reader’s knowledge

19. Fragment of reader’s prior knowledge: m3 = In “real life”, white is a color m6 = In “real life”, harts are deer m8 = In “real life”, deer are mammals m11 = In “real life”, halls are buildings m12 = In “real life”, b1 is named “King Arthur” m14 = In “real life”, b1 is a king(etc.)

20. m16 = if v3 has property v2 & if v2 is a color & if v3  v1 thenv1 is a kind of physical object

21. Reading the story: m17 = In the story, b2 is a hart m24 = In the story, the hart runs into b3 (b3 is King Arthur’s hall) – not shown (harts are deer) – not shown

22. The entire network showing the reader’s mental context consisting of prior knowledge, the story, & inferences. The definition algorithm searches this network & abstracts parts of it to produce a (preliminary) definition of ‘brachet’.

23. Implementation SNePS (Stuart C. Shapiro & SNeRG) : –Intensional, propositional semantic-network knowledge- representation & reasoning system –Formula-based & path-based reasoning I.e., logical inference & generalized inheritance –SNeBR belief revision system Used for revision of definitions –SNaLPS natural-language input/output –“Cassie”: computational cognitive agent

24. How It Works SNePS represents: – background knowledge + text information in a single, consolidated semantic network Algorithms deductively search network for slot-fillers for definition frame Search is guided by desired slots –E.g., prefers general info over particular info, but takes what it can get

25. Noun Algorithm Find or infer: Basic-level class memberships (e.g., “dog”, rather than “animal”) –else most-specific-level class memberships –else names of individuals Properties of Ns (else, of individual Ns) Structure of Ns (else …) Functions of Ns (else …) Acts that Ns perform (else …) Agents that perform acts w.r.t. Ns & the acts they perform (else…) Ownership Synonyms Else do: “syntactic/algebraic manipulation” “Al broke a vase”  a vase is something Al broke –Or: a vase is a breakable physical object

26. Verb Algorithm Infer: –properties of V –superclasses of V –transitivity information –similar actions (& delete dissimilar actions) –Conceptual-Dependency category –info about manner of V (“from”/“to”, transfer kind, instrument) –causes & effects Also return class membership of: –agent –object –indirect object –instrument [Also: preliminary work on adjective algorithm]

27. Belief Revision Used to revise definitions of words with different sense from current meaning hypothesis SNeBR (ATMS; Martins & Shapiro 88) : –If inference leads to a contradiction, then: 1.SNeBR asks user to remove culprit(s) 2.& automatically removes consequences inferred from culprit SNePSwD (SNePS w/ Defaults; Martins & Cravo 91) –Previously used to automate step 1, above; –Now, legacy code AutoBR (Johnson & Shapiro, in progress) & new default reasoner (Bhushan & Shapiro) –Will replace SNePSwD

28. Revision & Expansion Removal & revision being automated via SNePSwD by ranking all propositions with kn_cat: most intrinsic info re: language; fundamental background info certain(“before” is transitive) story info in text (“King Lot rode to town”) life background info w/o variables or inference (“dogs are animals”) story-comp info inferred from text (King Lot is a king, rode on a horse) life-rule.1 everyday commonsense background info (BearsLiveYoung(x)  Mammal(x)) life-rule.2 specialized background info (x smites y  x kills y by hitting y) least certain questionable already-revised life-rule.2; not part of input

29. Belief Revision: “smite” Misunderstood word; 2-stage “subtractive” revision Background knowledge includes: (*) smite(x,y,t)  hit(x,y,t) & dead(y,t) & cause(hit(x,y,t),dead(y,t)) P1: King Lot smote down King Arthur D1: If person x smites person y at time t, then x hits y at t, and y is dead at t Q1: What properties does King Arthur have? R1: King Arthur is dead. P2: King Arthur drew Excalibur. Q2: When did King Arthur do this? SNeBR is invoked: –KA’s drawing E is inconsistent with being dead –(*) replaced: smite(x,y,t)  hit(x,y,t) &  dead(y,t) & [dead(y,t)  cause(hit, dead)] D2: If person x smites person y at time t, then x hits y at t &  (y is dead at t) P3: [another passage in which ~(smiting  death)] D3: If person x smites person y at time t, then x hits y at t

30. Belief Revision: “dress” “additive” revision Background info includes: (1)dresses(x,y)   z[clothing(z) & wears(y,z) (2)Spears don’t wear clothing (both kn_cat=life.rule.1 ) P1: King Arthur dressed himself. D1: A person can dress itself; result: it wears clothing. P2: King Claudius dressed his spear. [Cassie infers: King Claudius’s spear wears clothing.] Q2: What wears clothing? SNeBR is invoked: –KC’s spear wears clothing inconsistent with (2). –(1) replaced: dresses(x,y)   z[clothing(z) & wears(y,z)] v NEWDEF –Replace (1), not (2), because of verb in antecedent of (1) (Gentner) P3: [other passages in which dressing spears precedes fighting] D2: A person can dress a spear or a person; result: person wears clothing or person is enabled to fight

31. Ongoing Research: From Algorithm to Curriculum more robust algorithms –better N coverage needed –much better V coverage needed –no general adjective/adverb coverage yet –need “internal” context (morphology, etc.) –need NL interface –need acting component need curriculum –CVA taught, but not well (emphasis on “guessing”) –we have explicit, teachable theory of how to do CVA –joint work w/ Michael Kibby, UB/LAI/Reading Clinic

32. State of the Art: Vocabulary Learning Some dubious contributions: Mueser 1984: “Practicing Vocabulary in Context” –BUT: “context” = definition !! Clarke & Nation 1980: a “strategy” (algorithm?) 1.Look at word & context; determine POS 2.Look at grammatical context E.g., “who does what to whom”? 3.Look at wider context [E.g., search for Sternberg-like clues] 4.Guess the word; check your guess

33. CVA: From Algorithm to Curriculum “guess the word” = “then a miracle occurs” Surely, we computer scientists can “be more explicit”!

34. Terminology: “Guessing”? Does reader … –“guess” a meaning?! not computational! –“deduce” a meaning? too restrictive; ignores other kinds of inference –“infer” a meaning? too vague; ignores other kinds of reasoning (cf. Herbert Simon) –“figure out” a meaning? just vague enough? My preference: –The reader computes a meaning!

35. Terminology: Co(n)text “co-text” or “textual context” = surrounding words “context” or “wide context” = –internalized co-text … –… integrated with … –… reader’s prior knowledge –“internalized” ≈ “mental model of” involves local interpretation (cf. McKoon & Ratcliff) –pronoun resolution, simple inferences (e.g., proper names) & global interpretation (“full” use of available PK) can involve misinterpretation (see later slide) –“integrated” via belief revision: new beliefs added by inference from text + prior knowledge old beliefs removed (usually from prior knowledge base)

36. Prior KnowledgeText PK1 PK2 PK3 PK4

37. Prior KnowledgeText PK1 PK2 PK3 PK4 T1

38. Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization

39. B-R Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization P5 inference

40. B-R Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization P5 inference T2 I(T2) P6

41. B-R Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization P5 inference T2 I(T2) P6 T3 I(T3)

42. B-R Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization P5 inference T2 I(T2) P6 T3 I(T3)

43. B-R Integrated KBText PK1 PK2 PK3 PK4 T1 I(T1) internalization P5 inference T2 I(T2) P6 T3 I(T3) P7 Note: All “contextual” reasoning is done in this “context”:

44. On Misinterpretation Sign seen on truck parked outside of cafeteria at Student Union: Mills Wedding and Specialty Cakes

45. On Misinterpretation Sign seen on truck parked outside of cafeteria at Student Union: Mills Welding and Specialty Gases

46. CVA as Science & Detection CVA = hypothesis generation & testing –scientific task: develop theory of word meaning not guessing, but… –“In science, guessing is called ‘hypothesis formation’ ” (Loui) –detective work: finding clues not “who done it”, but “what does it mean” –susceptible to revision upon further evidence

47. 2 Problematic Assumptions CVA assumes that: –reader is consciously aware of the unfamiliar word –reader notes its unfamiliarity CVA assumes that, between encounters: –reader remembers the word –reader remembers hypothesized meaning

48. I. Are All Contexts Created Equal? Beck, McKeown, & McCaslin (1983), –“Vocabulary Development: Not All Contexts Are Created Equal” –Elementary School Journal 83(3): 177-181. “it is not true that every context is an appropriate or effective instructional means for vocabulary development”

49. Role of Prior Knowledge Beck et al: –co-text “can give clues to the word’s meaning” But “clue” is relative: –clues need other info to be seen as clues Implication A1: –textual clues need to be supplemented with other information to compute a meaning. Supplemental info = reader’s prior knowledge –has to be accessible to reader –will be idiosyncratic  Co-text doesn’t suffice; prior knowledge needed

50. Do Words Have Unique, Correct Meanings? Beck et al. (& others) assume: –A2: A word has a unique meaning –A3: A word has a correct meaning Contra “unique”: A word’s meaning varies with: –co-text –reader(’s prior knowledge) –time of reading “Correct” is a red herring (in any case, it’s fishy): –Possibly, words have author-intended meanings but these need not be determined by context (textual or wide) –Misunderstandings are universally unavoidable * Perfect understanding/dictionary definition not needed understanding for passage comprehension suffices reader can always revise definition hypothesis

51. Beck et al.’s Categories of Textual Contexts What kinds of co-texts are helpful? But keep in mind that we have different goals: –Beck et al.: use co-text to teach “correct” word meanings –CCVA: use context to compute word meaning for understanding

52. Beck et al.’s Textual Context Categories Top-Level Kinds of Co-Text Pedagogical co-texts: –artificially constructed, designed for teaching Natural co-texts: –“not intended to convey the meaning of a word” –4 kinds (actually, a continuum)

53. Beck et al.’s Textual Context Categories Top-Level Kinds of Co-Text Pedagogical co-texts: –artificially constructed, designed for teaching –only example is for a verb: “All the students made very good grades on the tests, so their teacher commended them for doing so well.” Natural co-texts: –“not intended to convey the meaning of a word” –4 kinds (actually, a continuum)

54. Beck et al.’s Textual Context Categories 1. Misdirective (Natural) Co-Texts “seem to direct student to incorrect meaning for a word” sole example: –“Sandra had won the dance contest and the audience’s cheers brought her to the stage for an encore. ‘Every step she takes is so perfect and graceful,’ Ginny said grudgingly, as she watched Sandra dance.” –[[grudgingly]] =? admiringly Is this a natural context? Is this all there is to it?.. –A4: Co-texts have a fixed, usually small size –But larger co-text might add information –Prior knowledge can widen the context ‘grudgingly’ is an adverb! –A5: All words are equally easy to learn –But N easier than V, V easier than Adj/Adv! (Granger/Gentner/..Gleitman..) A6: Only 1 co-text can be used. –But later co-texts can assist in refining meaning

55. Beck et al.’s Textual Context Categories 2. Nondirective (Natural) Co-Texts “of no assistance in directing the reader toward any particular meaning for a word” sole example is for an adjective: –“Dan heard the door open and wondered who had arrived. He couldn’t make out the voices. Then he recognized the lumbering footsteps on the stairs and knew it was Aunt Grace.” But: –Is it natural? –What about larger co-text? –An adjective! –Of no assistance? (see next slide)

56. Syntactic Manipulation Misdirective & nondirective contexts can yield correct information! Cf. algebraic manipulation (brings x into focus):  2x + 1 = 7/  x = (7 − 1)/2 = 6/2 = 3 Syntactic manipulation (bring hard word into focus): “ ‘Every step she takes is so perfect and graceful,’ Ginny said grudgingly.” ‘Grudgingly’ is the way that Ginny said “…” So, ‘grudgingly’ is a way of saying something In particular, ‘grudgingly’ is a way of (apparently) praising someone’s performance “he recognized the lumbering footsteps on the stairs” ‘lumbering’ is a property of footsteps on stairs Generates initial hypothesis for later refinement

57. Beck et al.’s Textual Context Categories 3. General (Natural) Co-Texts “provide enough information for reader to place word in a general category” sole example is for an adjective: –“Joe and Stan arrived at the party at 7:00. By 9:30 the evening seemed to drag for Stan. But Joe really seemed to be having a good time at the party. ‘I wish I could be as gregarious as he is,’ thought Stan” Same problems, but: –adjective is contrasted with Stan’s attitude –contrasts are good (so are parallel constructions)

58. Beck et al.’s Textual Context Categories 4. Directive (Natural) Co-Texts “seem likely to lead the student to a specific, correct meaning for a word” sole example is for a noun: –“When the cat pounced on the dog, he leapt up, yelping, and knocked over a shelf of books. The animals ran past Wendy, tripping her. She cried out and fell to the floor. As the noise and confusion mounted, Mother hollered upstairs, ‘What’s all the commotion?’ ” Natural? Long! Noun! –note that the sole example of a directive context is a noun, suggesting that it might be the word that makes a context directive

59. Beck et al.’s Experiment S’s given passages from “basal” readers (reading textbooks) Researchers categorized co-texts & blacked out words S’s asked to “fill in the blanks with the missing words or reasonable synonyms” Results confirm 4 co-text types Independently of results, there are methodological questions: –Are basal readers natural contexts? –How large were co-texts? –Instruction on how to do CVA? A7: CVA “comes naturally”, so needs no training –A8: Fill-in-the-blank tasks are a form of CVA No, they’re not! (see next slide)

60. Beck et al.’s Experiment CVA, Neologisms, & Fill-in-the-Blank Serious methodological problem for all of us: –Replacing word with blank or neologism misleads S’s to find “correct missing/hidden word” ≠ CVA! Our (imperfect) solution: –use plausible-sounding neologism –tell S it’s like a foreign word with no English equivalent, hence need a descriptive phrase

61. Beck et al.’s Conclusion “less skilled readers … receive little benefit from” CVA A9: CVA can only help in learning correct meanings. But: –CVA uses same techniques as general reading comprehension: careful, slow reading careful analysis of text directed search for information useful for computing a meaning application of relevant prior knowledge application of reasoning for purpose of extracting information from text –  CVA, if properly taught & practiced, can improve general reading comprehension

62. II. Are Context Clues Unreliable Predictors of Word Meanings? Schatz & Baldwin (1986): –“Context Clues Are Unreliable Predictors of Word Meanings” –Reading Research Quarterly 21(4): 439-453. “context does not usually provide clues to the meanings of low-frequency words” “context clues inhibit the correct prediction of word meanings just as often as they facilitate them”

63. S&B’s Argument A10: CVA is not an efficient mechanism for inferring word meanings. Because: –Co-text can’t help you figure out the correct meaning of an unfamiliar word. –(uniqueness & correctness assumptions again!) But, we argue: –Wide context can help you figure out a meaning for an unfamiliar word. –So, context (& CVA) are efficient mechanisms for inferring (better: computing) word meanings.

64. Incidental vs. Deliberate CVA S&B: –“context clues should help readers to infer meanings of words without the need for readers to interrupt the reading act(*) with diversions to external sources” (*) true for incidental CVA (*) not for deliberate CVA External sources are no solution anyway: –Dictionary definitions are just more co-texts! (Schwartz 1988) –CVA is base case of recursion, one of whose recursive clauses is: “Look it up in a dictionary”

65. Why not use a dictionary? Because: People are lazy (!) Dictionaries are not always available Dictionaries are always incomplete Dictionary definitions are not always useful –‘chaste’ = df clean, spotless /  “new dishes are chaste” –‘college’ = df a body of clergy living together and supported by a foundation Most words are learned via incidental CVA, not via dictionaries Most importantly: –Dictionary definitions are just more contexts!

66. Why not use a dictionary? Merriam-Webster New Collegiate Dictionary: –chaste. 1.innocent of unlawful sexual intercourse –student: stay away from that one! 2.celibate –student: huh? 3.pure in thought and act: modest –student: I have to find a sentence for that? 4.a: severely simple in design or execution: austere –student: huh? “severely”? “austere”? b: clean, spotless –student: all right!: “The plates were still chaste after much use.” –Deese 1967 / Miller 1985

67. Why not use a dictionary? Merriam-Webster (continued): –college. 1.a body of clergy living together and supported by a foundation 2.a building used for an educational or religious purpose 3.a: a self-governing constituent body of a university offering living quarters and instruction but not granting degrees… b: a preparatory or high school c: an independent institution of higher learning offering a course of general studies leading to a bachelor’s degree… –Problem: ordering is historical!

68. Why not use a dictionary? Merriam-Webster (continued): –infract:infringe –infringe:encroach –encroach: to enter by gradual steps or by stealth into the possessions or rights of another to advance beyond the usual or proper limits; trespass

69. Why not use a dictionary? Collins COBUILD Dictionary –“Helping Learners with Real English” –chaste. 1.Someone who is chaste does not have sex with anyone, or only has sex with their husband or wife; an old-fashioned use, used showing approval. EG She was a holy woman, innocent and chaste. 2.Something that is chaste is very simple in style, without much decoration. EG …chaste houses built in 1732

70. Why not use a dictionary? Collins COBUILD Dictionary –college. 1.A college is 1.1 an institution where students study for qualifications or do training courses after they have left school. … infract [not in dictionary] infringe. 1.If you infringe a law or an agreement, you break it. encroach. 1.To encroach on or upon something means to slowly take possession or control of it, so that someone else loses it bit by bit.

71. S&B’s Experiments 25 natural passages from novels words chosen (the only cited examples): –Adj/Adv~67% –N~27% –V~ 6% But: –what are actual %s? –which lexical categories were hardest? –how do facilitative/confounding co-texts correlate with lexical category? –should have had representative sample of 4 co-text categories X 3 or 4 lexical categories

72. S&B’s Experiments CVA vs. Word-Sense Disambiguation 2 experiments: –S’s chose “correct” meaning from list of 5 possible meanings –This is WSD, not CVA! WSD = multiple choice CVA = essay question 3rd experiment: –real CVA, but “interested only in full denotative meanings or accurate synonyms” –cf. assumption A3 about “correct” meanings!

73. S&B’s Experiments Space & Time Limits Space limits on size of co-text? –S&B: 3 sentences –CCVA: start small, work “outward” Time limits on size of co-text? –S&B: “all students finished in allotted time” –CCVA: no time limits

74. S&B’s Experiments Teaching CVA S&B: “did not control for S’s knowledge of how to use context clues” CCVA: –deliberate CVA is a skill needs to be taught, modeled, & practiced –there is other (later) evidence that such training works including “critical thinking” education

75. S&B’s 3 Questions (answered in the negative) 1.“Do context clues occur with sufficient frequency to justify them as a major element of reading instruction?” a)Context clues do occur, & teaching them is justified, if augmented by reader’s prior knowledge & knowledge of CVA skills. 2.“Does context usually provide accurate clues to denotations & connotations of low-frequency words?” a)CVA can provide clues to revisable hypotheses about unfamiliar word’s meaning 3.Are “difficult words in natural [co-texts] usually amenable to such analysis?” a)Such words are always amenable to yielding at least some information about their meaning.

76. Our CVA Theory 1.Every co-text can give some clue to a word’s meaning. 2.Co-text clues must be supplemented by reader’s prior knowledge. a)Value of co-text depends on reader’s prior knowledge & ability to integrate them. 3.CVA ≠ fill-in-the-blank; CVA ≠ WSD 4.Co-text size has no arbitrary limits 5.May need lots of co-texts before CVA can asymptotically approach a stable meaning.

77. Our CVA Theory (continued) 6.A word does not have a unique meaning 7.A word does not have a correct meaning a)A word’s “correct” (intended) meaning does not need to be known in order for reader to understand it in context b)Even familiar/well-known words can acquire new meanings in new contexts. c)Neologisms usually are learned from context. 8.Some words are easier to compute meanings for than others (N < V < Adj/Adv) 9.CVA is an efficient method for computing word meanings. 10.CVA can improve general reading comprehension

78. Our CVA Theory (continued) 11. CVA can (and should) be taught! using a curriculum based on our algorithms

79. Teaching Computers vs. Teaching Humans Our goal: –Not: teach people to “think like computers” –But: to explicate computable & teachable methods to hypothesize word meanings from context AI as computational psychology: –Devise computer programs that are essentially faithful simulations of human cognitive behavior –Can tell us something about human mind. We are teaching a machine, to see if what we learn in teaching it can help us teach students better.