Contextual Vocabulary Acquisition: From Algorithm to Curriculum

1. Contextual Vocabulary Acquisition:From Algorithm to Curriculum Michael W. Kibby Department of Learning & Instruction and The Reading Center William J. Rapaport Department of Computer Science & Engineering Department of Philosophy, and Center for Cognitive Science Karen M. Wieland Department of Learning & Instruction , The Reading Center, and The Nichols School NSF ROLE Grant REC-0106338 1

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4. Why Learning Word Meanings Is Important 4

5. Why Learning Word Meanings Is Important Reason 1 National Assessment of Educational Progress-Reading(NAEP-Reading) 5

6. Meaning Vocabulary Assessment on NAEP-R Meaning vocabulary is the application of one’s understanding of word meanings to passage comprehension. 6

7. Vocabulary knowledge is considered to be one of the five essential components of reading as defined by the No Child Left Behind legislation. 7

8. NAEP will not test definitions in isolation from surrounding text; i.e., • students will not be assessed on their prior knowledge of definitions of words on a list. 8

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10. Altruistic Magnanimously Dispersed Impetus Forage Soothing Lost in thought Huddled Abide Piqued Beholden Marathon journey Legacy Abated Social contract Grudge Examples: 10

11. Why Learning Word Meanings Is ImportantReason 2 Learning new things and their words changes or increases our perception and organization of the world 14

12. The Lego™ Notion of Learning New Things 15

13. Why Learning Word Meanings Is Important Reason 3 • Reading comprehension mandates knowing the meaning (concept, thing) associated with words in the text. • When students do not know meanings of words in a written text, comprehension often disrupted. 16

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15. Why Learning Word Meanings Is ImportantReason 4 Learning new things and words facilitates students’ abilities to use words judiciously— which is much valued in our society 18

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18. Why Learning Word Meanings Is ImportantReason 5 The Profound Effects of Limited Vocabulary 21

19. Profound effects of limited vocabulary continued • Limited vocabulary is associated with lower IQ scores. • Limited vocabulary is associated with limited reading comprehension. • In grades 7+, vocabulary and reading comprehension correlate .75 to .85. 22

20. Social Class and Meaning Vocabulary Hart, Betty, & Risley, Todd R. (1995). Meaningful differences in the everyday experience of young children. Baltimore, MD: Brookes. 23

21. Studied 42 children’s vocabulary growth from their 9th month to their 36th month. • Researchers • Visited each child’s home once a month. • Observed and tape recorded for one hour every word spoken to or by child. • Recorded 23-30 hours for every child. 24

22. Actual and Estimated Number of Words Heard from 0 - 48 Months 25

23. “The Invisible Curriculum” 26

24. Cumulative Number of New Words (Hart & Risley, 1995) 27

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26. From Algorithm to Curriculum 29

27. Definition of “CVA” “Contextual Vocabulary Acquisition” =def • the acquisition of word meanings from text • “incidental” • “deliberate” • by reasoning about • contextual clues • background knowledge (linguistic, factual, commonsense) • Including hypotheses from prior encounters (if any) with the word • without external sources of help • No dictionaries • No people 47

28. CVA: From Algorithm to Curriculum • Computational theory of CVA • Based on: • algorithms developed by Karen Ehrlich (1995) • verbal protocols (case studies) • Implemented in a semantic-network-based knowledge-representation & reasoning system • SNePS (Stuart C. Shapiro & colleagues) • Educational curriculum to teach CVA • Based on our algorithms & protocols • To improve vocabulary & reading comprehension • Joint work with Michael Kibby & Karen Wieland • Center for Literacy & Reading Instruction 48

29. People Do “Incidental” CVA • We know more words than explicitly taught • Average high-school grad knows ~45K words  learned ~2.5K words/year (over 18 yrs.) • But only taught ~400/school-year • ~ 4800 in 12 years of school (~ 10% of total) • Most word meanings learned from context − including oral & perceptual contexts • “incidentally” (unconsciously) • How? 50

30. People Also Do “Deliberate” CVA • You’re reading; • You understand everything you read, until… • You come across a new word • Not in dictionary • No one to ask • So, you try to “figure out” its meaning from “context” • How? • guess? derive? infer? deduce? educe? construct? predict? … • our answer: • Compute it from inferential search of “context”, including background knowledge 51

31. What does ‘brachet’ mean? 52

32. (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] • As the hart went by the sideboard, the white brachet bit him.[66] • The knight arose, took up the brachet and rode away with the brachet.[66] • A lady came in and cried aloud to King Arthur, “Sire, the brachet is mine”.[66] • 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] 53

33. Computational cognitive theory of how to learn word meanings • From context • I.e., text + grammatical info + reader’s prior knowledge • With no external sources (human, on-line) • Unavailable, incomplete, or misleading • Domain-independent • But more prior domain-knowledge yields better definitions • “definition” = hypothesis about word’s meaning • Revisable each time word is seen 54

34. Cassie learns what “brachet” means:Background info about: harts, animals, King Arthur, etc.No info about: brachetsInput: formal-language (SNePS) version of simplified EnglishA 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) 55

35. --> (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 56

36. 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, 57

37. 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, 58

38. 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, 59

39. 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. 60

40. General Comments • System’s behavior  human protocols • System’s definition  OED’s definition: = A brachet is “a kind of hound which hunts by scent” 61

41. Computational cognitive theory of how to learn word meanings from context (cont.) • 3 kinds of vocabulary acquisition: • Construct new definition of unknown word • What does ‘brachet’ mean? • Fully revise definition of misunderstood word • Does “smiting” entail killing? • Expand definition of word used in new sense • Can you “dress” (i.e., clothe) a spear? • Initial hypothesis; Revision(s) upon further encounter(s); Converges to stable, dictionary-like definition; Subject to revision 62

42. State of the Art: Computational Linguistics • Information extraction systems • Autonomous intelligent agents • There can be no complete lexicon • Such systems/agents shouldn’t have to stop to ask questions 65

43. State of the Art: Computational Linguistics • Granger 1977: “Foul-Up” • Based on Schank’s theory of “scripts” (schema theory) • Our system not restricted to scripts • Zernik 1987: self-extending phrasal lexicon • Uses human informant • Ours system is really “self-extending” • Hastings 1994: “Camille” • Maps unknown word to known concept in ontology • Our system can learn new concepts • Word-Sense Disambiguation: • Given ambiguous word & list of all meanings, determine the “correct” meaning • Multiple-choice test  • Our system: given new word, compute its meaning • Essay question  66

44. State of the Art: Vocabulary Learning (I) • Elshout-Mohr/van Daalen-Kapteijns 1981,1987: • Application of Winston’s AI “arch” learning theory • (Good) reader’s model of new word = frame • Attribute slots, default values • Revision by updating slots & values • Poor readers update by replacing entire frames • But EM & vDK used: • Made-up words • Carefully constructed contexts • Presented in a specific order 67

45. Elshout-Mohr & van Daalen-Kapteijns Experiments with neologisms in 5 artificial contexts • When you are used to a view it is depressing when you live in a room with kolpers. • Superordinate information • At home he had to work by artificial light because of those kolpers. • During a heat wave, people want kolpers, so sun-blind sales increase. • Contexts showing 2 differences from the superordinate • I was afraid the room might have kolpers, but plenty of sunlight came into it. • This house has kolpers all summer until the leaves fall out. • Contexts showing 2 counterexamples due to the 2 differences 68

46. State of the Art: Psychology • Johnson-Laird 1987: • Word understanding  definition • Definitions aren’t stored • “During the Renaissance, Bernini cast a bronze of a mastiff eating truffles.” 69

47. State of the Art: Psychology • Sternberg et al. 1983,1987: • Cues to look for (= slots for frame): • Spatiotemporal cues • Value cues • Properties • Functions • Cause/enablement information • Class memberships • Synonyms/antonyms • To acquire new words from context: • Distinguish relevant/irrelevant information • Selectively combine relevant information • Compare this information with previous beliefs 70

48. Sternberg • The couple there on the blind date was not enjoying the festivities in the least. An acapnotic, he disliked her smoking; and when he removed his hat, she, who preferred “ageless” men, eyed his increasing phalacrosis and grimaced. 71

49. State of the Art: Vocabulary Learning (II) Some dubious contributions: • Mueser 1984: “Practicing Vocabulary in Context” • BUT: “context” = definition !! • Clarke & Nation 1980: a “strategy” (algorithm?) • Look at word & context; determine POS • Look at grammatical context • E.g., “who does what to whom”? • Look at wider context • [E.g., search for Sternberg-like clues] • Guess the word; check your guess 72

50. CVA: From Algorithm to Curriculum • “guess the word” = “then a miracle occurs” • Surely, we computer scientists can “be more explicit”! 73