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cva-727

cva-727.ppt. 20070122. Contextual Vocabulary Acquisition: From Algorithm to Curriculum. William J. Rapaport Department of Computer Science & Engineering, Department of Philosophy, Department of Linguistics, and Center for Cognitive Science rapaport@cse.buffalo.edu

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cva-727

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  1. cva-727.ppt 20070122

  2. Contextual Vocabulary Acquisition:From Algorithm to Curriculum William J. Rapaport Department of Computer Science & Engineering, Department of Philosophy, Department of Linguistics, and Center for Cognitive Science rapaport@cse.buffalo.edu http://www.cse.buffalo.edu/~rapaport

  3. Contextual Vocabulary Acquisition • Active, conscious acquisition of a meaning for a word in a text by reasoning from “context” • CVA = what you do when: • You’re reading • You come to an unfamiliar word • It’s important for understanding the passage • No one’s around to ask • Dictionary doesn’t help • No dictionary • Too lazy to look it up :-) • Word not in dictionary • Definition of no use • Too hard • Inappropriate • So, you “figure out” a meaning for the word “from context” • “figure out” = compute (infer) a hypothesis about what the word might mean in that text • “context” = ??

  4. Overview of CVA Project • From Algorithmto Curriculum • Implemented computational theory of how to figure out (compute) a meaning for an unfamiliar word from “wide context” • Based on: • algorithms developed by Karen Ehrlich (1995) • verbal protocols (case studies) • Implemented in a semantic-network-basedknowledge-representation & reasoning system • SNePS (Stuart C. Shapiro & colleagues)

  5. Overview of CVA Project (cont’d • From Algorithmto Curriculum • Convert algorithms to an improved, teachable curriculum • To improve vocabulary & reading comprehension • Joint work with Michael Kibby • Center for Literacy & Reading Instruction

  6. Meaning of “Meaning” • “the meaning of a word” vs. “a meaning for a word” • “the”  single, correct meaning • “of ”  meaning belongs to word • “a”  many possible meanings• depending on textual context, reader’s prior knowledge, etc. • “for”  reader hypothesizes meaning from “context”, & gives it to word

  7. “The meaning of things lies not in themselves but in our attitudes toward them.” • Antoine de Saint-Exupéry, Wisdom of the Sands (1948) • “Words don’t have meaning; they’re cues to meaning!” • Jeffrey L. Elman, “On Dinosaur Bones & the Meaning of Words” (2007) • “We cannot locate meaning in the text…; [this is an] active, dynamic process…, existing only in interactive behaviors of cultural, social, biological, and physical environment-systems.” • William J. Clancey, “Scientific Antecedents of Situated Cognition” (forthcoming)

  8. CVA as Cognitive Science • Studied in: • AI / computational linguistics • Psychology • Child-language development (L1 acquisition) • L2 acquisition (e.g., ESL) • Reading education (vocabulary development) • Thus far: “multi-”disciplinary • Not yet: “inter-”disciplinary!

  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] • As the hart went by the sideboard,the white brachet bit him.[66] • The knight arose, took up the brachet androde 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]

  10. Figure out meaning of word from what? • context (i.e., the text)? • Werner & Kaplan 52, McKeown 85, Schatz & Baldwin 86 • context and reader’s background knowledge? • Granger 77, Sternberg 83, Hastings 94 • context including background knowledge? • Nation & Coady 88, Graesser & Bower 90 • Note: • “context” = text  context is external to reader’s mind • Could also be spoken/visual/situative (still external) • “background knowledge”: internal to reader’s mind • What is (or should be) the “context” for CVA?

  11. What Is the “Context” for CVA? • “context” ≠ textual context • surrounding words; “co-text” of word • “context” = wide context = • “internalized” co-text … • ≈ reader’s interpretive mental model of textual “co-text” • involves local interpretation (McKoon & Ratcliff): proN resol’n, simple infs (prop names) • & global interpretation (“full” use of available PK) • can involve misinterpretation • … “integrated” via belief revision … • infer new beliefs from internalized co-text + prior knowledge • remove inconsistent beliefs • … with reader’s prior knowledge: • “world” knowledge • language knowledge • 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

  12. Some Proposed Preliminary Definitions(to extract order out of confusion) • Unknown word for a reader =def • Word or phrase that reader has never seen before • Or only has vague idea of its meaning • Different levels of knowing meaning of word • Notation: “X”

  13. Proposed preliminary definitions • Text =def • (written) passage • containing X • single phrase or sentence … several paragraphs

  14. Proposed preliminary definitions • Co-text of X in some text =def • The entire text “minus” X; i.e., entire text surrounding X • E.g., if X = ‘brachet’, and text = • “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.” Then X’s co-text in this text = • “There came a white hart running into the hall with a white ______ next to him, and thirty couples of black hounds came running after them.” • Cf. “cloze” tests in psychology • But, in CVA, reader seeks meaning or definition • NOT a missing word or synonym: There’s no “correct” answer! • “Co-text” is what many mean by “context” • BUT: they shouldn’t!

  15. Proposed preliminary definitions • The reader’s prior knowledge =def • the knowledge that the reader has when s/he begins to read the text • and is able to recall as needed while reading • “knight picks up & carries brachet” ? small • Warnings: • “knowledge”  truth • so, “prior beliefs” is better • “prior” vs. “background” vs. “world”, etc. • See next slide!

  16. Proposed preliminary definitions • Possible synonyms for “prior knowledge”, each with different connotation: • Background knowledge: • Can use for information that author assumes reader to have • World knowledge: • General factual knowledge about things other than the text’s topic • Domain knowledge: • Specialized, subject-specific knowledge about the text’s topic • Commonsense knowledge: • Knowledge “everyone” has • E.g., CYC, “cultural literacy” (Hirsch) • These overlap: • PK should include some CSK, might include some DK • BK might include much DK

  17. Steps towards aProper Definition of “Context” • Step 1: • The context of X for a reader =def • The co-text of X • “+” the reader’s prior knowledge • Both are needed! • After reading: • “the white brachet bit the hart in the buttock” most subjects infer that brachets are (probably) animals, from: • That text, plus: • Available PK premise: “If x bites y, then x is (probably) an animal. • Inference is not an enthymeme! (because …)

  18. Proper definition of “context”: • But (inference not an enthymeme because): • When you read, you “internalize” the text • You “bring it into” your mind • Gärdenfors 1997, 1999; Jackendoff 2002 • This “internalized text” is more important than the actual words on paper: • Text: “I’m going to put the cat out” • Misread as: “I’m going to put the car out” • leads to different understanding of “the text” • What matters is what the reader thinks the text is, • Not what the text actually is • Therefore …

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

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

  21. Proper definition of “context”: • Step 2: • The context of X for a reader =def • A single KB, consisting of: • The reader’s internalized co-text of X • “+” the reader’s prior knowledge

  22. Proper definition of “context”: • But: What is “+”? • Not: mere conjunction or union! • Active readers make inferences while reading. • From text = “a white brachet” & prior commonsense knowledge = “only physical objects have color”, reader might infer that brachets are physical objects • From “The knight took up the brachet and rode away with the brachet.” & prior commonsense knowledge about size, reader might infer that brachet is small enough to be carried • Whole > Σ parts: • inference from [internalized text + PK]  new info not in text or in PK • I.e., you can learn from reading!

  23. Proper definition of “context”: • But: Whole <Σ parts! • Reader can learn that some prior beliefs were mistaken • Or: reader can decide that text is mistaken (less likely) • Reading & CVA need belief revision! • operation “+”: • input: PK & internalized co-text • output: “belief-revised integration” of input, via: • Expansion: • addition of new beliefs from ICT into PK, plus new inferences • Revision: • retraction of inconsistent prior beliefs together with inferences from them • Consolidation: • eliminate further inconsistencies

  24. Prior Knowledge Text PK1 PK2 PK3 PK4

  25. Prior Knowledge Text T1 PK1 PK2 PK3 PK4

  26. Integrated KB Text T1 internalization PK1 PK2 PK3 PK4 I(T1)

  27. B-R Integrated KB Text T1 internalization PK1 PK2 PK3 PK4 I(T1) inference P5

  28. B-R Integrated KB Text T1 internalization PK1 PK2 PK3 PK4 I(T1) T2 inference P5 I(T2) P6

  29. B-R Integrated KB Text T1 internalization PK1 PK2 PK3 PK4 I(T1) T2 inference T3 P5 I(T2) P6 I(T3)

  30. B-R Integrated KB Text T1 internalization PK1 PK2 PK3 PK4 I(T1) T2 inference T3 P5 I(T2) P6 I(T3)

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

  32. Note: All “contextual” reasoning is done in this “context”: B-R Integrated KB (the reader’s mind) Text T1 internalization PK1 PK2 PK3 PK4 P7 I(T1) T2 inference T3 P5 I(T2) P6 I(T3)

  33. Proper definition of “context”: • One more detail: X needs to be internalized • Context is a 3-place relation among: • Reader, word, and text • Final(?) def.: • Let T be a text • Let R be a reader of T • Let X be a word in T (that is unknown to R) • Let T-X be X’s co-text in T. • Then: • The context that R should use to hypothesize a meaning for R’s internalization of X as it occurs in T =def • The belief-revised integration of R’s prior knowledge with R’s internalization of T-X.

  34. This definition agrees with… • Cognitive-science & reading-theoretic views of text understanding • Schank 1982, Rumelhart 1985, etc. • & KRR techniques for text understanding: • Reader’s mind modeled by KB of prior knowledge • Expressed in KR language (for us: SNePS) • Computational cognitive agent reads the text, • “integrating” text info into its KB, and • making inferences & performing belief revision along the way • When asked to define a word, • Agent deductively searches this single, integrated KB for information to fill slots of a definition frame • Agent’s “context” for CVA = this single, integrated KB

  35. Distinguishing Prior Knowledge from Integrated Co-Text • So KB can be “disentangled” as needed for belief revision or to control inference: • Each proposition in the single, integrated KB is marked with its “source”: • Originally from PK • Originally from text • Inferred • Sources of premises

  36. Some Open Questions • Roles of spoken/visual/situative contexts • Relation of CVA “context” to formal theories of context (e.g., McCarthy, Guha…) • Relation of I(T) to prior-KB; e.g.: • Is I(Ti) true in prior-KB? • It is “accepted pro tem”. • Is I(T) a “subcontext” of pKB or B-R KB? • How to “activate” relevant prior knowledge. • Etc.

  37. Background of CVA Project • People do “incidental” (unconscious) 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 • Students are taught “deliberate” (conscious) CVAin order to improve their vocabulary

  38. 1. Computational CVA • Implemented in SNePS (Shapiro 1979; Shapiro & Rapaport 1992) • Intensional, propositional semantic-networkknowledge-representation, reasoning, & acting system • Indexed by node: From any node, can describe rest of network • Serves as model of the reader (“Cassie”) • KB: SNePS representation of reader’s prior knowledge • I/P: SNePS representation of word in its co-text • Processing (“simulates”/“models”/is?! reading): • Uses logical inference, generalized inheritance, belief revisionto reason about text integrated with reader’s prior knowledge • N & V definition algorithms deductively search this “belief-revised, integrated” KB (the context) for slot fillers for definition frame… • O/P: Definition frame • slots (features): classes, structure, actions, properties, etc. • fillers (values): info gleaned from context (= integrated KB)

  39. 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; PK: Cassie believes that only physical objects have color)

  40. --> (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

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

  42. 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. [PK: Only small things can be picked up/carried] --> (defineNoun "brachet") Definition of brachet: Class Inclusions: animal, Possible Actions: bite buttock, Possible Properties: small, white, Possibly Similar Items: mammal, pony,

  43. 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. [PK: Only valuable things are wanted]--> (defineNoun "brachet") Definition of brachet: Class Inclusions: animal, Possible Actions: bite buttock, Possible Properties: valuable, small, white, Possibly Similar Items: mammal, pony,

  44. 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. [PK: 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.

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

  46. 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

  47. Fragment of reader’s prior knowledge: m3 = In “real life”, white is a color Member(Lex(white),Lex(color),LIFE) m6 = In “real life”, harts are deer AKO(Lex(hart),Lex(deer),LIFE) m8 = In “real life”, deer are mammals AKO(Lex(deer),Lex(mammal),LIFE) m11 = In “real life”, halls are buildings AKO(Lex(hall),Lex(building),LIFE) m12 = In “real life”, b1 is named “King Arthur” Name(b1,”King Arthur”,LIFE) m14 = In “real life”, b1 is a king Isa(ISA,b1,Lex(king),LIFE) (etc.)

  48. m16 = if v3 has property v2 & v2 is a color & v3  v1 then v1 is a class of physical objects all(x,y,z)({Is1(z,y),Member1(y,lex(color)),Member1(z,x)} &=> {AKO1(x,lex(physical object))})

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

  50. A fragment of the entire network showing the reader’s mental context consisting of prior knowledge, the story, & inferences. The definition algorithm searches this entire network,abstracts parts of it, & produces a hypothesized meaning for ‘brachet’.

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