Contextual Vocabulary Acquisition:
From Algorithm to Curriculum
Michael W. Kibby, Ph.D.
Department of Learning & Instruction and The Reading Center
William J. Rapaport, Ph.D.
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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Criteria for Selecting Vocabulary Items and Distractors for the NAEP
Reading Assessme nt



Words Selected for
Inclusion
Are central to the
meaning of the
passage, such that
lack of understanding
may disrupt
comprehension
May have multiple
meanings, but only
one meaning will be
appropriate in the
passage
Are likely to be found
in grade level reading
material
Words Excluded from
Selection
 Are technical terms
(e.g., photosynthesis,
fiduciary)
 Convey the main idea
of the passage (e.g.,
eminent domain)
 Are already part of the
studentХs speaking
vocabulary
Criteria for Distractors



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May be other
meanings of the target
vocabulary word
May present other
information or content
from the text but do
NOT present what is
meant by the target
word
May contain other
words that look or
sound lsimilar to the
text word
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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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Three Reasons NAEP-R
Does Not Test a Specific
Word List
1. Knowledge of the explicit definition
of a word is not what is required for
reading comprehension.
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2. The meaning of a word is too often
dependent upon the context.
e.g. cast
• The fisherman cast his line.
• The members of the cast took a bow.
• They put a cast on my broken arm.
• The yard is littered with shells cast off
by the cicadas.
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3. Writers often use words in a
manner that goes beyond their
concrete, familiar definition, but do
so in ways that skilled readers can
interpret effectively.
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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 (i.e., concept,
thing) associated with words in the
text
• When students do not know
meanings of words in a written text,
comprehension often decreases.
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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
40
Actual
30
Professional
Working
Welfare
20
10
25
Age in Months
48
45
42
39
36
33
30
27
24
21
18
15
12
9
6
3
0
0
Actual & Estimated Words
Addressed to Child
Estimated
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“The Invisible Curriculum”
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Cumulative Vocabulary Words
Cumulative Number of New
Words (Hart & Risley, 1995)
1200
1000
800
Professional
Working
Welfare
600
400
200
0
10
14
18
22
26
30
Age of Child in Months
27
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A Brief Background on the
Counting of Words
• Carroll, Davies, Richman (1971), The
American Heritage Word Frequency
Book–called the WFI.
• A count of 5,088,721 different words
(called tokens) in printed English for
grades 3-9.
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Of 5,088,721 Words in WFI
• There were 86,741 different words.
• But the following 13 were counted as
different words:
add
additive
additives
adds
addition
additions
added
addend
addends
adding
additional
ADDITION
as well as Add (capitalized).
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When Do Two Words Differ?
• Nagy & Anderson sampled WFI words.
• Put each word in 1 of 6 classes varying
in semantic relation to other words.
– Classes 0, 1, 2 closely related semantically.
– Classes 3, 4, 5 progressively more distant.
• Estimated there are 139,020 different
words in semantic categories 0, 1, & 2.
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• But 45,453 of these are base words—
knowing these 45,453 means a reader
knows all 139,020.
• Adding 43,080 in classes 3, 4 & 5 brings
total to 88,583 different word families in
printed school texts for grades 3-9.
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Learning New Words is
Natural
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Edna Heidbretter, The
Attainment of Concepts.
1946
• taught persons to associate nine
pairs of visual shapes and
pronounceable pseudo word
• told persons this was a memory task
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pran
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mulf
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Test
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SETS
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Trials
to Learn
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SETS
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SETS
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SETS
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SETS
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SETS
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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 55
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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,
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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
• Vocabulary Learning:
– Some dubious contributions:
• Useless “algorithms”
• Contexts that include definition
– Useful contribution:
• (good) reader’s word-model
= updateable frame with slots & defaults
• Psychology:
– Cues to look for (= slots for frame):
• Space, time, value, properties, functions, causes, classes,
synonyms, antonyms
– Can understand a word w/o having a definition
• Computational Linguistics:
– Systems need scripts, human informants, ontologies
• Not needed in our system
– CVA  Word-Sense Disambiguation
• Essay question vs. multiple-choice test
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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”
•
•
72
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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Why not use a dictionary?
•
Merriam-Webster New Collegiate Dictionary:
– chaste.
1. innocent of unlawful sexual intercourse
– student: stay away from that one!
2. celibate
– student: huh?
3. pure in thought and act: modest
– student: I have to find a sentence for that?
4. a: severely simple in design or execution:
austere
– student: huh? “severely”? “austere”?
b: clean, spotless
– student: all right!: “The plates were still chaste after
much use.”
–
Deese 1967 / Miller 1985
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Why not use a dictionary?
•
Merriam-Webster (continued):
– college.
1. a body of clergy living together and supported by
a foundation
2. a building used for an educational or religious
purpose
3. a: a self-governing constituent body of a
university offering living quarters and
instruction but not granting degrees…
b: a preparatory or high school
c: an independent institution of higher
learning offering a course of general
studies leading to a bachelor’s degree…
– Problem: ordering is historical!
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Why not use a dictionary?
• Merriam-Webster (continued):
– infract:
– infringe:
– encroach:
infringe
encroach
• to enter by gradual steps or by stealth into
the possessions or rights of another
• to advance beyond the usual or proper
limits; trespass
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Why not use a dictionary?
•
Collins COBUILD Dictionary
– “Helping Learners with Real English”
– chaste.
1. Someone who is chaste does not have sex
with anyone, or only has sex with their
husband or wife; an old-fashioned use,
used showing approval. EG She was a holy
woman, innocent and chaste.
2. Something that is chaste is very simple in
style, without much decoration. EG
…chaste houses built in 1732
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Why not use a dictionary?
•
Collins COBUILD Dictionary
–
college.
1. A college is 1.1 an institution where students study for
qualifications or do training courses after they have left
school. …
•
•
infract [not in dictionary]
infringe.
1. If you infringe a law or an agreement, you break it.
•
encroach.
1. To encroach on or upon something means to slowly
take possession or control of it, so that someone else
loses it bit by bit.
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Question (objection):
Teaching computers  teaching humans!
But:
• Our goal:
– Not: teach people to “think like computers”
– But: to explicate computable & teachable methods to
hypothesize word meanings from context
• AI as computational psychology:
– Devise computer programs that are essentially faithful
simulations of human cognitive behavior
– Can tell us something about human mind.
• We are teaching a machine, to see if what we learn
in teaching it can help us teach students better.
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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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Belief Revision
•
Used to revise definitions of words with different
sense from current meaning hypothesis
SNeBR (ATMS; Martins & Shapiro 88):
•
–
If inference leads to a contradiction, then:
1.
2.
•
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
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Revision & Expansion
• Removal & revision being automated via SNePSwD by ranking all propositions
with kn_cat:
most
certain
intrinsic
story
least
certain
info re: language; fundamental background info
(“before” is transitive)
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)
questionable already-revised life-rule.2; not part of input
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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
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Belief Revision: “dress”
•
•
“additive” revision
Bkgd info includes:
(1) dresses(x,y)  z[clothing(z) & wears(y,z)
(2) Spears don’t wear clothing (both kn_cat=life.rule.1)
P1: King Arthur dressed himself.
D1: A person can dress itself; result: it wears clothing.
P2: King Claudius dressed his spear.
[Cassie infers: King Claudius’s spear wears clothing.]
Q2: What wears clothing?
•
SNeBR is invoked:
–
–
–
KC’s spear wears clothing inconsistent with (2).
(1) replaced: dresses(x,y)  z[clothing(z) & wears(y,z)] v NEWDEF
Replace (1), not (2), because of verb in antecedent of (1) (Gentner)
P3: [other passages in which dressing spears precedes fighting]
D2: A person can dress a spear or a person;
result: person wears clothing or person is enabled to fight
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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”:
• 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.
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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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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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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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CVA & Science Education
• Original goal: CVA in & for science education
– Use CVA to improve reading of STEM materials
• A side effect: CVA as science education
– There are no ultimate authorities to consult
• No answers in the back of the book of life!
• As true for STEM as it is for reading about STEM
–  Goal of education =
• To learn how to learn on one’s own
• Help develop confidence & desire to use that skill
– CVA as scientific method in miniature furthers this goal:
•
•
•
•
Find clues/evidence (gathering data)
Integrate them with personal background knowledge
Use together to develop new theory (e.g., new meaning)
Test/revise new theory (on future encounters with word)
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Conclusion
Developing a computational theory of CVA,
which can become …
• a useful educational technique for improving
vocabulary and reading comprehension
• a model of the scientific method
• a useful tool for learning on one’s own.
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An Analysis of Think-Aloud
Protocol of Good Readers
Using CVA Strategies
During Silent Reading
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Facilitating Vocabulary
Growth
•
•
•
•
•
Word fun.
Roots, prefixes, affixes.
Dictionary.
Wide reading.
Contextual vocabulary acquisition–
CVA.
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Limited Data Describing
CVA Processes
•
•
•
•
•
•
Nation–guess.
Ames.
Deighton.
Sternberg.
Elshout-Mohr & van Daalen-Kapteijns.
Harmon.
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Overview of Our Study
• We asked good readers to thinkaloud when they encountered a word
whose meaning they did not know as
they silently read a set of 7-17 texts,
each text containing at least one
instance of the unknown word.
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Overview continued
• We analyzed the think-aloud protocol
to gain understanding of the CVA
processes.
• Besides understanding CVA
processes, our goal was to build a
more effective CVA curriculum.
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Beginning and Ending
Research Questions
• What text cues are used for CVA?
• What are CVA reasoning processes?
• What sense or meaning of an
unknown word is gained from CVA?
• How is information from prior
encounters with a hard word used
when the word is seen in new texts?
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Methodology: Hard Words
• Identified small set of “hard words.”
– Some words from Ehrlich &
Rapaport earlier work with Cassie.
– Dale & O’Rourke and Carroll, Davis
& Richman as a guide.
– Some words came from scanning
science and current event texts.
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Methodology: Text Sets
• For each word, identified a set of 717 authentic texts, each with 1 or
more instances of the word.
• Hard words were in boldface font.
• Sometimes, replaced real “hard
word” with a neologism (a nonword): 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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Word
apple
guttate
Г
I have neve r
heard this
word
Г
Г
Г
I have hea rd
I know this
I know this
this word, but word when I word and can
do not know it hear or read it use it when
in a sentence
writing or
speaking
Example
Г
Г
Vocabulary
estimated
pentimento
cornucopia
reproduce
estuary
aglet
arrested
sedate
proliferate
meadow
taciturn
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Sample Words
Word
Г
I know
this word
apple
Г
Definition or Sentence Using the Word
Apples are a fruit, usually red and grow on
trees.
guttate
Vocabulary Words
Word
Г
I know
this word
1.
Definition or Sentence Using the Word
estimated
2.
pentimento
3.
cornucopia
4.
reproduce
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Methodology: Procedures
• Worked 1-1 with researcher.
• Read each passage, one at a time.
• Researcher provided meaning of
other words in text reader did not
know.
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Procedures continued
• When hard word encountered, reader
thought aloud while trying to gain
sense of word’s meaning.
• Audio tape recorded think-alouds.
• Transcribed taped think-alouds.
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Methodology: Analyses of
Verbal Protocol
•
•
•
•
Processing of texts and hard words.
Use of external context cues in CVA.
Reasoning processes in CVA.
Sense of word meaning gained from
CVA.
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Research Assumptions:
Good Readers
• Will know and be able to apply
multiple CVA processes.
• Are wide readers who have—
incidentally or deliberately—learned
many words from reading.
• Will have excellent comprehension of
text.
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Research Assumptions: CVA
Processes
• CVA processes are a set of substrategies activated by a disruption
of text processing caused by
encountering an unknown word.
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Research assumptions: CVA processes
continued
• CVA shares many characteristics of
reading comprehension (e.g., use of
selective text cues, prior knowledge,
reasoning), but triggers text
processing strategies different than
ordinary comprehension fix-up
strategies.
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Research Assumptions:
CVA for Word Learning and
Reading Comprehension
• Most readers apply CVA processes to
gain meaning from the text, therefore
gain word meanings —incidentally.
• We ask readers to try to gain a sense
of the unknown word’s meaning —
deliberately.
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Research Assumption:
Conditional Factors Needed
for Applying CVA
• Disrupted comprehension is
required, or reader may just skip
word.
• Word awareness: i.e., reader must
note that there is a hard word (Reed,
1957; Harmon, 1999).
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Overview of Findings:
1. Processing of texts and hard words
in CVA.
2. Use of text cues in CVA.
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Overview of findings continued
3. Reasoning processes in CVA.
a. Hypotheses or model building.
b. Inferential / abstract reasoning from
reading comprehension.
c. Within-sentence language cues.
d. Information processing / knowledge
acquisition processes (Sternberg,
1987)
e. Global strategies.
f. Prior knowledge in CVA processes.
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Overview of findings continued
4. Sense of the word meaning from
CVA.
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Findings–1:Processing
Texts and Hard Words
• After encountering hard word, some
readers seemed to continue to read
the full passage, then returning to
the word to work on its meaning.
• Some readers stopped reading upon
the hard word (or read to the end of
the sentence), and worked on the
word meaning immediately.
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Findings–2:
Use of External Text Cues
• First, we classified the textual cues
in texts using Ames (1969), Deighton
(1978), Sternberg & Powell (1983),
Ehrlich (1995), and Sternberg (1987).
• Second, we analyzed the think-aloud
protocol to see if these were the
clues readers used.
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Use of external text cues continued
• When reading and encountering an
unknown word, readers generally
started CVA with reasoning
processes.
• Sometimes they went back to text,
sometimes they did not.
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Use of external text cues continued
• After forming a hypothesis, some
readers reinspected text to find
support for hypothesis.
• Some readers created a hypothesis
using general passage and sentence
meaning, but did not go back to text.
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Use of external text cues continued
• Others said there was nothing in text
to help them gain a sense of word
meaning.
• Other readers did not go back to text
at all unless prompted.
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Use of external text cues continued
Readers inferred a sense of the
word on the basis of:
– general passage meaning,
– meaning of the specific sentence,
– sentence language and syntax,
– prior knowledge, and
– prior passages.
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Use of external text cues continued
• When readers did refer to the
passage for specific information:
– Usually to confirm a hypothesis.
– Did not generally select the
sentences we had predicted they
would use.
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These Findings Lead Us in
Three Directions:
• An unpredicted, but–with hindsight–
logical conclusion.
• Curricular implications.
• Abandoning coding “available” cues in
the text to coding reasoning processes
applied to word meaning.
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Why is it Readers Did Not First
Reinspect Text for Cues?
• They did not know the word’s meaning, so those
text cues had no particular salience for the
reader.
• For the researcher’s, however, these cues were
salient, because we already knew the word’s
meaning.
• Readers varied in what they accepted as a
sufficient hypothesis.
• We did not teach readers specific cues to look
for–wanted to see what they did independently.
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• That is, when a reader knows a
word’s meaning, that word’s
connection to all the cues in the text
is obvious.
• But when one does not know the
meaning of the word, one does not
readily discern the cues that provide
insight to the word’s meaning.
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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: Reasoning
Processes in CVA
• Hypotheses or model building
• Inferring / abstract reasoning from
reading comprehension
• Language cues
• Global strategies
• Background knowledge
• Conclusion
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Findings: Hypothesis
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
Daalen-Kapteijns’ Good
Readers
• Our readers generally modified
hypotheses in keeping with text.
• Our readers seemed to be aware that
they did not really “know” the word’s
meaning, that what they knew was a
hypothesis.
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Unlike Deegan (1995 )
• Our readers rarely altered the
meaning of text to stay in keeping
with prior hypothesis.
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Within Sentence Language
Cues
• Familiar expressions
• Figurative language
• Connected series
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Inferring / Abstract Reasoning
from Reading Comprehension
• Encoding selected information.
• Combining selected text information.
• Comparing selected information from
text to background knowledge.
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Global Comprehension
Strategies
•
•
•
•
•
•
Visualizing.
Summarizing.
Clarifying.
Self questioning.
Insight.
Confirming / confidence.
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Background Knowledge
• Essential
• Idiosyncratic
• Pervasive
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Findings–4: Sense of the
Word Meaning from CVA
• Right and wrong are not useful
descriptors of appropriateness of
word meanings from CVA processes.
• Rational and defensible are better
descriptors of appropriateness than
is right or wrong.
• Gradual and cumulative over texts.
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Sense of the word continued
0. No meaning provided.
1. Don’t Know (dk)
2. Incorrect–No logical justification for
sense of word.
3. Incorrect–Reasonable justification
for sense of word proffered.
4. Incorrect–Based on language
patterns, not general text meaning.
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Sense of the word continued
5. Vague or partial word meaning
sense.
6. Approximate word meaning sense.
7. Nearly correct word meaning sense.
8. Correct sense of word meaning.
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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
• Teacher modeling of CVA strategies
in texts with words whose meanings
are not known.
• Scaffolding groups as they together
think-aloud when applying CVA
strategies in texts with words whose
meanings are not known.
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How to improve CVA continued
• Guiding small groups in think-alouds
of CVA strategies in texts with words
whose meanings are not known.
• Student’s independent application of
CVA strategies in texts with words
whose meanings are not known.
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Protocol Study Limitations
• Reading done in a research
environment.
• 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.
• Gained knowledge and insight on
teaching CVA.
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Developing Meaning
Vocabulary in Classroom
• There is no one way to teach meaning
vocabulary, there is no best method.
• Helping children build their meaning
vocabularies is a philosophy that
shapes classroom culture and
interaction, not a specific lesson plan.
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Preliminary to
Recommendations
•
•
•
•
CVA.
Dictionaries.
Roots and affixes.
Direct teaching of vocabulary.
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Preliminary to recommendations
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Rationale for Our
Recommendations
• On average, students learn 3000
words a year (Nagy & Herman, 1987)
• At most, students can be directly
taught 300-400 words a year.
• Ergo, students learn 2,700 words per
year outside of direct instruction.
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Rationale for recommendations continued
• That is, 10% of the words students
learn per year on average may be
directly taught.
• And, no less than 90% (and probably
more) are learned by means other
than direct teaching.
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Rationale for recommendations, continued
• This means that the extra effort in
direct teaching of words will result in
only an additional 50± words. Very
little gain for so great an amount of
instructional time.
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Recommendations
• Therefore, to promote vocabulary
growth, maximize methods of
learning word meanings other than
dictionaries, morphology, direct
instruction, and–even–deliberate
contextual vocabulary acquisition.
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Overview of
Recommendations
•
•
•
•
Word consciousness.
Word curiosity.
Word fun.
Teacher modeling and guided
student practice in CVA.
• Wide reading.
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