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 1

2. Definition of “CVA” the acquisition of word meanings from text
“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

3. 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

4. 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

5. 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

6. What does ‘brachet’ mean?
Move into small groups: If <= 20, then divide into 3 groups, facilitated by Bill, Michael, & Karen
If > 20, then: save 5 seats in front;
choose 5 people at random for demo 52

7. (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]
Predefine ‘hart’, ‘sideboard’, ‘bay’.
Sentence 3: if no one suggests that brachet is small, ask how big it is. 53

8. 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

9. Cassie learns what “brachet” means: Background info about:
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) 55

10. --> (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

11. A hart runs into King Arthur’s hall
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

12. 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

13. A hart runs into King Arthur’s hall
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

14. --> (defineNoun "brachet") Definition of brachet:
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

15. 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

16. 3 kinds of vocabulary acquisition:
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

17. 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

18. 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 
Ellen Prince 66

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

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

26. CVA: From algorithm to curriculum …
Treat “guess” as a procedure call (“subroutine”)
Fill in the details with our algorithm
Convert the algorithm into a curriculum
To enhance students’ abilities to use deliberate CVA strategies
To improve reading comprehension
… and back again!
Use knowledge gained from CVA case studies to improve the algorithm
I.e., use Cassie to learn how to teach humans
& use humans to learn how to teach Cassie 74

27. 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! 75

28. 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 82

29. 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.) 83

30. then v1 is a kind of physical object
m16 = if v3 has property v2
& if v2 is a color
& if v3  v1
then v1 is a kind of physical object 84

31. m17 = In the story, b2 is a hart
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 85

32. 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’. 86

33. Implementation SNePS (Stuart C. Shapiro & SNeRG):
Intensional, propositional semantic-network knowledge-representation & reasoning system
Node-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 87

34. 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 88

35. 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 89

36. Verb Algorithm Find or infer: Future work:
Predicate structure:
Categorize arguments/cases
Results of V’ing:
Effects, state changes
Enabling conditions for V
Future work:
Classification of verb-type
Synonyms
[Also: preliminary work on adjective algorithm] 90

37. 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:
SNeBR asks user to remove culprit(s)
& automatically removes consequences inferred from culprit
SNePSwD (SNePS w/ Defaults; Martins & Cravo 91)
Currently used to automate step 1, above
AutoBR (Johnson & Shapiro, in progress)
& new default reasoner (Bhushan & Shapiro, in progress)
Will replace SNePSwD
OMIT IF NO TIME 91

38. 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
OMIT IF NO TIME 92

39. 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 93

40. Belief Revision: “dress”
“additive” revision
Bkgd info includes:
dresses(x,y)  z[clothing(z) & wears(y,z)
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 94

41. 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? 95

42. 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” 96

43. Proposed preliminary definitions
Text =def
(written) passage
containing X
single phrase or sentence … several paragraphs 97

44. 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! 98

45. 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! 99

46. 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
100

47. Steps towards a Proper Definition of “Context”
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!
(argument with missing premise)
101

48. Proper definition of “context”:
(inference not an enthymeme because):
When you read, you “internalize” the text
You “bring it into” your mind
Gärdenfors 1997, 1999; Jackendoff 2002
“Missing” premise might be in reader’s mind!
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 …
102

49. 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
103

50. 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 > sum of parts:
inference from [internalized text + PK]  new info not in text or in PK
I.e., you can learn from reading!
104

51. Proper definition of “context”:
But: Whole < sum of 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
105

52. Prior Knowledge
Text
PK1
PK2
PK3
PK4
106

53. Prior Knowledge
Text T1
PK1
PK2
PK3
PK4
107

54. Integrated KB
Text T1
PK1
PK2
PK3
PK4
internalization
I(T1)
108

55. B-R Integrated KB Text T1 PK1 PK2 PK3 PK4 I(T1) P5 inference
internalization T1
PK1
PK2
PK3
PK4
I(T1)
inference P5
109

56. B-R Integrated KB Text T1 PK1 PK2 PK3 PK4 I(T1) T2 P5 I(T2) P6
internalization
I(T1) T2
inference P5
I(T2) P6
110

57. B-R Integrated KB Text PK1 PK2 PK3 PK4 T1 I(T1) T2 T3 P5 I(T2) P6
internalization T1
I(T1) T2
inference T3 P5
I(T2) P6
I(T3)
111

58. B-R Integrated KB Text PK1 PK2 PK3 PK4 T1 I(T1) T2 T3 P5 I(T2) P6
internalization T1
I(T1) T2
inference T3 P5
I(T2) P6
I(T3)
112

59. Note: All “contextual” reasoning is done in this “context”:
B-R Integrated KB
Text T1
PK1
PK2
PK3
PK4
internalization P7
I(T1) T2
inference T3 P5
I(T2) P6
I(T3)
113

60. Proper definition of “context”:
The context that reader R should use to hypothesize a meaning for R’s internalization of unknown word X as it occurs in text T =def
The belief-revised integration of R’s prior knowledge with R’s internalization of the co-text T–X.
114

61. This definition agrees with…
Cognitive-science & reading-theoretic views of text understanding
Schank 1982, Rumelhart 1985, etc.
& AI techniques for text understanding:
Reader’s mind modeled by KB of prior knowledge
Expressed in AI 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
116

62. 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.
118

63. Problem in Converting Algorithm into Curriculum
“A knight picks up a brachet and carries it away …”
Cassie:
Has “perfect memory”
Is “perfect reasoner”
Automatically infers that brachet is small
People don’t always realize this:
May need prompting: How big is the brachet?
May need relevant background knowledge
May need help in drawing inferences
Teaching CVA =? teaching general reading comprehension
Vocabulary knowledge correlates with reading comprehension
119

64. Research Methodology AI team: Reading team:
Develop, implement, & test better computational theories of CVA
Translate into English for use by reading team
Reading team:
Convert algorithms to curriculum
Think-aloud protocols
To gather new data for use by AI team
As curricular technique (case studies)
120

65. Web Page
126