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
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Why Learning Word
Meanings Is Important
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Why Learning Word Meanings Is Important
Reason 1
National Assessment of
Educational ProgressReading
(NAEP-Reading)
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Meaning Vocabulary
Assessment on NAEP-R
Meaning vocabulary is the application
of one’s understanding of word
meanings to passage comprehension.
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• Vocabulary knowledge is considered
to be one of the five essential
components of reading as defined by
the No Child Left Behind legislation.
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• 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.
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C ri teria fo r Selec ting V o ca bu lary I tem s a nd D is trac to rs fo r th e NA EP
R ea d ing A ssessme nt
Wo rd s Selec te d fo r
In cl u si on



A re cent ra l to the
m ean ing of the
passage, such th
at
lack of unde rstand in g
m ay d is rupt
co m p rehens ion
M ay have m u ltip le
m ean ings, but on ly
one m ean ing will be
app rop ri ate in the
passage
A re like ly to be found
in g rade leve l r ead in g
m ate ri a l
Wo rd s Excl ud e d f ro m
Selec tion



A re techn ica l te rm s
(e.g., photosynthes
is,
fiduc ia ry)
C onvey the m a in ide a
of the passage
(e. g .,
e mi nent do m a in )
A re a lready pa rt of th e
student Õ
s speak ing
vocabu la ry
C ri teria fo r D is trac to rs



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M ay be othe r
m ean ings of the ta rg et
vocabu la ry w o rd
M ay p resent othe r
info rm at ion o r content
fro m the text but do
N O T p resent w hat is
m eant by the ta rget
w o rd
M ay conta in othe r
w o rds that look o r
sound lsimil a r to the
text w o rd
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Examples:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Altruistic
Magnanimously
Dispersed
Impetus
Forage
Soothing
Lost in thought
Huddled
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Abide
Piqued
Beholden
Marathon journey
Legacy
Abated
Social contract
Grudge
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Why Learning Word Meanings Is Important
Reason 2
Learning new things and their words
changes or increases our perception
and organization of the world
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The Lego™ Notion of
Learning New Things
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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.
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Why Learning Word Meanings Is Important
Reason 4
Learning new things and words
facilitates students’ abilities to use
words judiciously— which is much
valued in our society
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Why Learning Word Meanings Is Important
Reason 5
The Profound Effects of
Limited Vocabulary
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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.
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Social Class and
Meaning Vocabulary
Hart, Betty, & Risley, Todd R. (1995).
Meaningful differences in the
everyday experience of young
children. Baltimore, MD: Brookes.
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• 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.
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Actual and Estimated
Number of Words Heard
from 0 - 48 Months
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In
Millions
Estimated
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Working
Welfare
30
Actual
20
10
25
Age in Months
48
45
42
39
36
33
30
27
24
21
18
15
12
9
6
3
0
0
A ctu al & E s tim ated W o rd s
A d d ressed to C hild
Professional
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“The Invisible Curriculum”
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Cumulative Number of New
Words (Hart & Risley, 1995)
1200
1000
800
600
400
200
0
10
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From Algorithm to
Curriculum
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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
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CVA: From Algorithm to Curriculum
1. 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)
2. 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
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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?
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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
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What does ‘brachet’ mean?
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(From Malory’s Morte D’Arthur [page # in brackets])
1.
2.
3.
4.
10.
18.
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]
The hart lay dead; a brachet was biting on his
throat, and other hounds came behind. [86]
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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
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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)
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--> (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
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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,
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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,
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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,
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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.
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General Comments
• System’s behavior  human protocols
• System’s definition  OED’s definition:
= A brachet is “a kind of hound which hunts by scent”
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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
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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
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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 
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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
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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
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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.”
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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
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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.
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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?)
1.
2.
Look at word & context; determine POS
Look at grammatical context
•
3.
Look at wider context
•
4.
E.g., “who does what to whom”?
[E.g., search for Sternberg-like clues]
Guess the word; check your guess
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CVA: From Algorithm to Curriculum
“guess the word”
=
“then a miracle occurs”
•
•
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Surely,
we computer scientists
can “be more explicit”!
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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
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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!
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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
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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.)
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m16 = if
& if
& if
then
v3 has property v2
v2 is a color
v3  v1
v1 is a kind of physical object
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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
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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’.
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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
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Verb Algorithm
• Find or infer:
– 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]
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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?
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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”
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Proposed preliminary definitions
• Text =def
– (written) passage
– containing X
– single phrase or sentence … several
paragraphs
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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!
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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!
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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
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Steps towards a
Proper Definition of “Context”
•
Step 1:
–
The context of X for a reader =def
1. The co-text of X
2.
•
“+” 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)
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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:
Misread as:
“I’m going to put the cat out”
“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 …
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Proper definition of “context”:
• Step 2:
– The context of X for a reader =def
• A single KB, consisting of:
1. The reader’s internalized co-text of X
2.
“+”
the reader’s prior knowledge
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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!
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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
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Prior Knowledge
Text
PK1
PK2
PK3
PK4
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Prior Knowledge
Text
T1
PK1
PK2
PK3
PK4
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Integrated KB
Text
internalization
PK1
T1
I(T1)
PK2
PK3
PK4
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B-R Integrated KB
Text
internalization
PK1
T1
I(T1)
PK2
inference
PK3
P5
PK4
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B-R Integrated KB
Text
internalization
PK1
I(T1)
T1
T2
PK2
inference
PK3
P5
PK4
I(T2)
P6
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B-R Integrated KB
Text
internalization
PK1
I(T1)
T1
T2
PK2
inference
PK3
T3
P5
PK4
I(T2)
P6
I(T3)
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B-R Integrated KB
Text
internalization
PK1
I(T1)
T1
T2
PK2
inference
PK3
T3
P5
PK4
I(T2)
P6
I(T3)
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Note: All “contextual” reasoning is done in this “context”:
B-R Integrated KB
internalization
PK1
P7
Text
I(T1)
T1
T2
PK2
inference
PK3
T3
P5
PK4
I(T2)
P6
I(T3)
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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.
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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
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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
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Research Methodology
• AI 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)
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5-Minute Intermission
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An Analysis of
Think-Aloud Protocol of
Good Readers Using
CVA Strategies
During Silent Reading
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Limited Data Describing
CVA Processes
• Ames.
• Deighton.
• Harmon.
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Goals of Our Study
• Understand CVA processes.
– Improve Cassie (computational model).
– Build a more effective CVA curriculum.
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Beginning and Ending
Research Questions
• What text cues are used for CVA?
• What cognitive processes are used
in CVA, including activation of
background information?
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Research questions continued
• What sense or meaning of an
unknown word is gained from CVA?
• Do prior textual encounters with a
hard word affect CVA in a new text?
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Methodology: Hard Words
• Identified small set of “hard words.”
– Some words from earlier work with
Cassie.
– Dale & O’Rourke and Carroll, Davis
& Richman used as guides.
– Some words came from scanning
science and current event texts.
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Methodology: Text Sets
• For each hard word:
– identified a set of 7-17 authentic texts,
– 1-2 instances of each word in each text.
• Sometimes, replaced hard word with
a neologism (a non-word): e.g.,
itresia for estuary
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Methodology: Participants
• High school students.
• Excellent or outstanding readers.
• Readers given pre-test.
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Methodology: Procedures
• Worked 1-1 with researcher.
• Told there was an unknown word.
• Asked to construct a dictionary-like
definition of the highlighted target word.
• Read each passage, one at a time.
• Researcher explained meaning of other
unknown words in text.
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Procedures continued
• When hard word encountered, reader
thought aloud while trying to gain
sense of word’s meaning.
• Sessions recorded on audio tape.
• Sessions transcribed verbatim.
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Our Procedures Vary from Normal
CVA Processes and Conditions
• CVA usually activated by disruption of
comprehension / text processing.
• Then . . .
– Reader notes there is a hard word in the text.
– Reader notes that lack of that word’s meaning
disrupts comprehension.
– Reader decides to figure out meaning of word.
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Methodology:
Analyses of Verbal Protocol
• Analyzed transcribed verbal protocol
over and over again.
• Began by coding.
• Changed coding many times.
• Right now, still coding some data,
but mostly writing multi-page
summaries of each protocol.
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Overview of
Four Sets of Findings
1. Approaches to CVA.
2. Text cues and use of text cues for
CVA.
3. Cognitive processes in CVA.
4. Sense of word meaning from CVA.
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Findings–1:
Readers’ Approaches to CVA
• Most read entire text, then returned to
target word to work on its meaning.
• Some looked first at the target
sentence, then read entire text.
• A few stopped reading at the target
word or end of sentence to work on
word meaning immediately.
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Findings–2:
Use of Text Cues
•
•
Generally, readers did not initially
re-inspect text for cues.
Rather, think-alouds usually started
with readers hypothesizing a
meaning on basis of general
passage comprehension and
background knowledge.
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Use of text cues continued
• As readers read further passages and
were more confirmed in their
hypothesis, many then did reinspect
text to find support for their
hypothesis.
• In this re-inspection, readers generally
did not select the sentences we had
predicted they would use.
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These Findings Lead Us in
Three Directions:
• To an unanticipated, but logical,
conclusion about use of text cues.
• To curricular implications.
• To change our focus from examining
the expected use of context cues to
identifying the cognitive processes
emerging from the data.
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Why Was Rereading for Text
Cues Not a Reader’s 1st Step?
• Researchers knew text cues that
provided useful information, but we
knew the hard word’s meaning.
• Readers did not know the hard word’s
meaning, so the relevant text cues had
no particular salience for them.
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Our Conclusion
Using Context for CVA is Easy When
You Already Know Meaning of Word!
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Curricular Implications
• Teachers should model CVA with
words they do not know in texts they
have not previously seen.
• Students should practice CVA with
words they do not know in texts they
have not previously seen.
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Findings-3:
Cognitive Processes in CVA
1. Generated hypothesis / built model.
2. Drew inferences from general passage
meaning and prior knowledge.
3. Used language cues.
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Cognitive Processes in CVA-continued
4. Used information processing/knowledge
acquisition processes (Sternberg, 1987).
5. Used knowledge from prior CVA texts.
6. Closure was rare.
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CVA Cognitive Processes–1:
Hypothesis / Model Building
• All readers hypothesized a meaning
of the hard word.
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On Further Encounters With
Word, Readers . . .
• Confirmed hypothesis if congruent
with text, usually stating rationale;
• Revised hypothesis if not congruent,
usually stating rationale; or
• If hypothesis not congruent with text,
but not enough information in text to
revise it, readers questioned
hypothesis.
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Like Elshout-Mohr & Van DaalenKapteijns’ Good Readers
• Our readers generally modified
hypotheses in keeping with text.
• Our readers appeared to “know” that
what they stated was a tentative
hypothesis.
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Unlike Werner & Kaplan (1952)
& Deegan (1995)
• It was rare for a reader to force text
meaning to fit a previous hypothesis
about word meaning.
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CVA Cognitive Processes–2:
Inferences from General Passage
Meaning and Prior Knowledge
• Prior knowledge was:
– Essential.
– Idiosyncratic.
– Pervasive.
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CVA cognitive processes continued:
• Observed Inferencing Strategies
– Visualizing.
– Clarifying.
– Self questioning.
– Insight.
– Summarizing.
– Confirming / confidence.
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CVA Cognitive Processes–3:
Use of Language Cues
• Familiar expressions
• Figurative language
• Connected series
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CVA Cognitive Processes–4:
Sternberg’s Information Processing
• Separating relevant and irrelevant
information.
• Combining relevant cues from text.
• Comparing new text information with
hypothesized meaning and prior
knowledge.
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CVA Cognitive Processes–5:
Knowledge from Prior CVA Texts
• Content of previous texts became
part of prior knowledge.
• Readers used this prior knowledge
from previous texts in subsequent
texts.
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CVA Cognitive Processes–6:
Closure was Rare
• Tentativeness of hypothesis was
generally maintained.
• Students rarely felt they had solid
knowledge of word’s meaning.
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Findings–4: Sense of Intended
Word Meaning from CVA
• Right and wrong are not appropriate
descriptors when contrasting meanings
from CVA to intended meaning.
• Rational and defensible are better
descriptors than right or wrong.
• Gradual and cumulative over texts.
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Sense of the word continued
1. Don’t Know (dk) any meaning.
2. Not intended–No logical justification
for sense of word.
3. Not intended–Reasonable justification
for sense of word proffered.
4. Not intended–Based on language
patterns, not general text meaning.
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Sense of the word continued
5. Vague or partial sense of intended
meaning.
6. Approximate sense of intended
meaning.
7. Nearly full sense of intended meaning.
8. Full intended meaning.
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Protocol Study Limitations
• We required reader to think about the
unknown word and its meaning.
– Readers ordinarily might choose to skip
or ignore word.
– Readers ordinarily might not note word.
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Protocol limitations continued
• Our readers encountered word in
multiple, consecutive texts.
– Therefore, readers had immediate
memory of the previous encounter.
– This is atypical, as readers ordinarily
might not encounter new word a second
time for a long period.
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Protocol limitations continued
• We sometimes ask leading
questions: e.g.,
– To activate background knowledge.
– To direct reader back to text.
– To elicit reasoning processes.
– To ask reader to think again.
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Protocol limitations continued
• At times, we used non-words in place
of real words: e.g.,
schmalion for tatterdemalion
vedosarn for taciturn
itresia for estuary.
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Protocol Study Strengths
• Used authentic texts.
• Words were, generally, difficult and
not previously known by readers.
• Used one hard word in repeated
texts, not multiple hard words each
in a different text.
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How to Improve CVA in
Classrooms
Guess?
Magical Mathematical Formula?
CVA Strategies!
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How to improve CVA continued
1. CVA curriculum should help students
learn to apply reasoning and prior
knowledge to textual cues.
a. Read passage for full comprehension.
b. Draw inferences from language, meaning,
and prior knowledge.
c. Summarize into a meaning-hypothesis
frame.
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How to improve CVA continued
2. Teachers:
a. Model CVA with words whose
meanings are unknown.
b. Scaffold groups applying CVA
strategies with words whose
meanings are unknown.
c. Guide small groups in think-alouds of
CVA.
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How to improve CVA continued
3. Students:
– Independently apply CVA.
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Developing Meaning
Vocabulary in Classroom
• Most important is building a culture
that fosters interest in words.
– Leads to word learning naturally.
– Facilitates life-long interest and wordlearning abilities.
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Recommendations
• Such classrooms have a culture that
stimulates:
– Word consciousness.
– Word curiosity.
– Word fun.
– Wide reading.
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Rationales for Our
Recommendation
1. We know learning words is natural!
• Fully 90% (2,700±) of words learned
per year are learned incidentally,
outside of direct school instruction.
• Directly teaching words results in a
gain of few words–e.g., 50-100.
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Rationale for recommendations, continued
• But facilitating attraction and
attentiveness to words students hear
or see in their normal daily life could
increase natural word learning by
100s, if not 1000s.
• These cognitive and affective factors
promoted by such a culture will
serve the student well for a lifetime.
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Rationale for recommendations, continued
2. We know a child’s environment
profoundly affects vocabulary
growth.
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Actual and Estimated
Number of Words Heard
from 0 - 48 Months
50
In
Millions
Estimated
40
Working
Welfare
30
Actual
20
10
179
Age in Months
48
45
42
39
36
33
30
27
24
21
18
15
12
9
6
3
0
0
A ctu al & E s tim ated W o rd s
A d d ressed to C hild
Professional
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Cumulative Number of New
Words (Hart & Risley, 1995)
1200
1000
800
600
400
200
0
10
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QUESTIONS AND
DISCUSSION
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CVA slide show - University at Buffalo, Computer Science and