Department of Computer Science & Engineering,
Department of Philosophy, and Center for Cognitive Science
State University of New York at Buffalo
Buffalo, NY 14260 http://www.cse.buffalo.edu/~rapaport/CVA/

Wisdom of the Sands (1948)

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

• CVA project:
– computational theory of how to “figure out”
“a meaning for” an “unfamiliar word” from “context”.
• Current status:
– Have theory
– Have computational implementation
• Know that people do it.
• Possibly best explanation of how we learn vocabulary
– given # of words known (~45K),
& # of years to learn them (~18) = ~2.5K words/year
– but only taught ~10% in 12 school years
• 2 groups of researchers say CVA can’t be done (well)
• This talk: Why they’re wrong.

• CVA = active, deliberate acquisition of a meaning for a word in a text by reasoning from “context”
• context =
– “internalized” co-text
• roughly: reader’s mental model of surrounding words in text
– “integrated” with reader’s prior knowledge
• including language knowledge
• including previous hypotheses about word’s meaning
– but not including external sources (dictionary, humans)

(From Malory’s Morte D’Arthur [page # in brackets])
1. There came a white hart running into the hall with a white brachet next to him, and thirty couples of black hounds came running after them .
[66]
2.
As the hart went by the sideboard, the white brachet bit him.
[66]
3.
The knight arose, took up the brachet and rode away with the brachet.
[66]
4.
A lady came in and cried aloud to King Arthur, “Sire, the brachet is mine”.
[66]
10. There was the white brachet which bayed at him fast.
[72]
18 . The hart lay dead; a brachet was biting on his throat, and other hounds came behind.
[86]

• Based on Karen Ehrlich’s CS Ph.D. dissertation (1995)
• Implemented in SNePS KRRA system
• KB: SNePS representation of reader’s prior knowledge
• I/P: SNePS representation of word & co-text
• Processing:
– inferences drawn/belief revision during text input
– N & V definition algorithms deductively search this
“belief-revised, integrated” KB (the context) for definitional information
• O/P: Definition frame
– slots (features): classes, structure, actions, properties, etc.
– fillers (values): info gleaned from context (= integrated KB)

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)

--> (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

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 ,

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,

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,

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.

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

• SNePS
(Stuart C. Shapiro & SNeRG)
:
– Intensional, propositional semantic-network knowledgerepresentation & reasoning system
– Formula-based & path-based reasoning
• I.e., logical inference & generalized inheritance
– SNeBR belief revision system
• Used for revision of definitions
– SNaLPS natural-language input/output
– “Cassie”: computational cognitive agent

• SNePS represents:
– background knowledge + text information in a single, consolidated semantic network
• Algorithms deductively search network for slot-fillers for definition frame
• Search is guided by desired slots
– E.g., prefers general info over particular info, but takes what it can get

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

Verb Algorithm
• Infer:
– properties of V
– superclasses of V
– transitivity information
– similar actions (& delete dissimilar actions)
– Conceptual-Dependency category
– info about manner of V (“from”/“to”, transfer kind, instrument)
– causes & effects
• Also return class membership of:
– agent
– object
– indirect object
– instrument
• [Also: preliminary work on adjective algorithm]

• more robust algorithms
– better N coverage needed
– much better V coverage needed
– no general adjective/adverb coverage yet
– need “internal” context (morphology, etc.)
– need NL interface
– need acting component
• need curriculum
– CVA taught, but not well (emphasis on “guessing”)
– we have explicit, teachable theory of how to do CVA
– joint work w/ Michael Kibby, UB/LAI/Reading Clinic

State of the Art: Vocabulary Learning
Some dubious contributions:
• Mueser 1984: “Practicing Vocabulary in Context”
– BUT: “context” = definition !!
• Clarke & Nation 1980: a “strategy” (algorithm?)
1.
Look at word & context; determine POS
2.
Look at grammatical context
• E.g., “who does what to whom”?
3.
Look at wider context
• [E.g., search for Sternberg-like clues]
4.
Guess the word; check your guess

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

• Does reader …
– “guess” a meaning?!
• not computational!
– “deduce” a meaning?
• too restrictive; ignores other kinds of inference
– “infer” a meaning?
• too vague; ignores other kinds of reasoning (cf. Herbert Simon)
– “figure out” a meaning?
• just vague enough?
• My preference:
–
The reader computes a meaning!

• “co-text” or “textual context” = surrounding words
• “context” or “wide context” =
– internalized co-text …
– … integrated with …
– … reader’s prior knowledge
– “internalized” ≈ “mental model of”
• involves local interpretation (cf. McKoon & Ratcliff)
– pronoun resolution, simple inferences (e.g., proper names)
• & global interpretation (“full” use of available PK)
• can involve mis interpretation (see later slide)
– “integrated” via belief revision:
• new beliefs added by inference from text + prior knowledge
• old beliefs removed (usually from prior knowledge base)

Prior Knowledge
PK1
PK2
PK3
PK4
Text

Prior Knowledge
PK1
PK2
PK3
PK4
Text
T1

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

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

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

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

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

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

• Sign seen on truck parked outside of cafeteria at Student Union:

• Sign seen on truck parked outside of cafeteria at Student Union:

• CVA = hypothesis generation & testing
– scientific task:
• develop theory of word meaning
• not guessing, but…
– “In science, guessing is called ‘hypothesis formation’ ” (Loui)
– detective work:
• finding clues
• not “who done it”, but “what does it mean”
– susceptible to revision upon further evidence

• CVA assumes that:
– reader is consciously aware of the unfamiliar word
– reader notes its unfamiliarity
• CVA assumes that, between encounters:
– reader remembers the word
– reader remembers hypothesized meaning

•
Beck , Isabel L.; McKeown , Margaret G.; &
McCaslin , Ellen S. (1983),
– “Vocabulary Development:
Not All Contexts Are Created Equal”
–
Elementary School Journal 83(3): 177-181.
• “it is not true that every context is an appropriate or effective instructional means for vocabulary development”

• Beck et al:
– co-text “can give clues to the word’s meaning”
• But “clue” is relative:
– clues need other info to be interpreted as clues
• Implication A1:
– textual clues need to be supplemented with other information to compute a meaning.
• Supplemental info = reader’s prior knowledge
– has to be available to reader
– will be idiosyncratic

Do Words Have Unique, Correct Meanings?
• Beck et al. (& others) assume:
– A2: A word has a unique meaning
–
A3: A word has a correct meaning
• Contra “unique”: A word’s meaning varies with:
– co-text
– reader(’s prior knowledge)
– time of reading
• “Correct” is a red herring (in any case, it’s fishy):
– Possibly, words have author-intended meanings
• but these need not be determined by co(n)text
– Misunderstandings are universally unavoidable
– Perfect understanding/dictionary definition not needed
• “satisficing” understanding for passage comprehension suffices
• reader always has opportunity of revising definition hypothesis

• What kinds of co-texts are helpful?
• But keep in mind that we have different goals:
– Beck et al.:
• use co-text to teach “correct” word meanings
– CCVA:
• use context to compute word meaning for understanding

Beck et al.’s Textual Context Categories
•
Pedagogical co-texts:
– artificially constructed, designed for teaching
– only example is for a verb:
• “All the students made very good grades on the tests, so their teacher commended them for doing so well.”
•
Natural co-texts:
– “not intended to convey the meaning of a word”
– 4 kinds (actually, a continuum)

Beck et al.’s Textual Context Categories
• “seem to direct student to incorrect meaning for a word”
• sole example:
– “Sandra had won the dance contest and the audience’s cheers brought her to the stage for an encore. ‘Every step she takes is so perfect and graceful,’
Ginny said grudgingly , as she watched Sandra dance.”
– [[grudgingly]] =? admiringly
• Is this a natural context?
• Is this all there is to it?..
–
A4: Co-texts have a fixed, usually small size
– But larger co-text might add information
– Prior knowledge can widen the co(n)text
• ‘grudgingly’ is an adverb!
– A5: All words are equally easy to learn
– But N easier than V, V easier than Adj/Adv! (Granger/Gentner/..Gleitman..)
•
A6: Only 1 co-text can be used.
– But later co-texts can assist in refining meaning

Beck et al.’s Textual Context Categories
• “of no assistance in directing the reader toward any particular meaning for a word”
• sole example is for an adjective:
– “Dan heard the door open and wondered who had arrived.
He couldn’t make out the voices. Then he recognized the lumbering footsteps on the stairs and knew it was Aunt
Grace.”
• But:
– Is it natural?
– What about larger co-text?
– An adjective!
– Of no assistance? (see next slide)

• Do misdirective & nondirective contexts yield no (or only incorrect) information?
• Cf. algebraic manipulation (brings x into focus):

2 x + 1 = 7
 x = (7 − 1)/2 = 6/2 = 3
• Syntactic manipulation (bring hard word into focus):
• “ ‘Every step she takes is so perfect and graceful,’ Ginny said grudgingly. ”
• ‘Grudgingly’ is the way that Ginny said “…”
• So, ‘grudgingly’ is a way of saying something
• In particular, ‘grudgingly’ is a way of (apparently) praising someone’s performance
• “he recognized the lumbering footsteps on the stairs”
• ‘lumbering’ is a property of footsteps on stairs

Beck et al.’s Textual Context Categories
• “provide enough information for reader to place word in a general category”
• sole example is for an adjective:
– “Joe and Stan arrived at the party at 7:00. By 9:30 the evening seemed to drag for Stan. But Joe really seemed to be having a good time at the party. ‘I wish I could be as gregarious as he is,’ thought Stan”
• Same problems, but:
– adjective is contrasted with Stan’s attitude
– contrasts are good (so are parallel constructions)

Beck et al.’s Textual Context Categories
• “seem likely to lead the student to a specific, correct meaning for a word”
• sole example is for a noun:
– “When the cat pounced on the dog, he leapt up, yelping, and knocked over a shelf of books. The animals ran past
Wendy, tripping her. She cried out and fell to the floor. As the noise and confusion mounted, Mother hollered upstairs,
‘What’s all the commotion
?’ ”
• Natural? Long!
• Noun!
– note that the sole example of a directive context is a noun, suggesting that it might be the word that makes a context directive

• S’s given passages from basal readers
• Researchers categorized co-texts & blacked out words
• S’s asked to “fill in the blanks with the missing words or reasonable synonyms”
• Results confirm 4 co-text types
• Independently of results, there are methodological questions:
– Are basal readers natural contexts?
– How large were co-texts?
– Instruction on how to do CVA?
• A7: CVA “comes naturally”, so needs no training
– A8: Fill-in-the-blank tasks are a form of CVA
• No, they’re not! (see next slide)

Beck et al.’s Experiment
• Serious methodological problem for all of us:
– What if S knows the unknown word?
– Filter out such S’s and words?
• hard to do; what about testing familiar words?
– Replace word with made-up “neologism”?
• must be carefully chosen
– Replace word with blank?
• both kinds of replacement mislead S to find
“correct missing/hidden word”
• ≠ CVA!
• Our (imperfect) solution:
– use plausible-sounding neologism
– tell S it’s like a foreign word with no English equivalent, hence need a descriptive phrase

• “less skilled readers … receive little benefit from” CVA
•
A9: CVA can only help in learning correct meanings.
• But:
– CVA uses same techniques as general reading comprehension:
• careful, slow reading
• careful analysis of text
• directed search for information useful for computing a meaning
• application of relevant prior knowledge
• application of reasoning for purpose of extracting information from text
– 
CVA, if properly taught & practiced, can improve general reading comprehension

•
Schatz , Elinor; & Baldwin , R. Scott (1986):
– “Context Clues Are Unreliable Predictors of Word Meanings”
–
Reading Research Quarterly 21(4): 439-453.
• “context does not usually provide clues to the meanings of low-frequency words”
• “context clues inhibit the correct prediction of word meanings just as often as they facilitate them”

•
A10: CVA is not an efficient mechanism for inferring word meanings.
• Because:
– Co-text can’t help you figure out the correct meaning of an unfamiliar word.
– (assumptions A2 & A3 again!)
• But:
–
Wide context can help you figure out a meaning for an unfamiliar word.
– So, context (& CVA) are efficient mechanisms for inferring
(better: computing) word meanings.

• S&B:
– “context clues should help readers to infer meanings of words without the need for readers to interrupt the reading act (*) with diversions to external sources”
• (*) true for incidental CVA
• (*) not for deliberate CVA
• External sources are no solution anyway:
– Dictionary definitions are just more co-texts!
(Schwartz 1988)
– CVA is base case of recursion, one of whose recursive clauses is: “Look it up in a dictionary”

• 25 natural passages from novels
• words chosen (the only cited examples):
– Adj/Adv
– N
– V
• But:
~67%
~27%
~ 6%
– what are actual %s?
– which lexical categories were hardest?
– how do facilitative/confounding co-texts correlate with lexical category?
– should have had representative sample of
4 co-text categories
X
3 or 4 lexical categories

S&B’s Experiments
CVA vs. Word-Sense Disambiguation
• 2 experiments:
– S’s chose “correct” meaning from list of 5 possible meanings
– This is WSD, not CVA!
• WSD = multiple choice
• CVA = essay question
• 3rd experiment:
– real CVA, but “interested only in full denotative meanings or accurate synonyms”
– cf. assumption A3 about “correct” meanings!

S&B’s Experiments
• Space limits on size of co-text?
– S&B: 3 sentences
– CCVA: start small, work “outward”
• Time limits on size of co-text?
– S&B: “all students finished in allotted time”
– CCVA: no time limits

S&B’s Experiments
• S&B: “did not control for S’s knowledge of how to use context clues”
• CCVA:
– deliberate CVA is a skill
• needs to be taught, modeled, & practiced
– there is other (later) evidence that such training works
• including “critical thinking” education

(answered in the negative)
1.
“Do context clues occur with sufficient frequency to justify them as a major element of reading instruction?” a) Irrelevant if CVA fosters good reading comprehension & critical thinking skills b) Context clues do occur & teaching them is justified, if augmented by reader’s prior knowledge & knowledge of CVA skills.
2.
“Does context usually provide accurate clues to denotations & connotations of low-frequency words?” a) Irrelevant under our conception of CVA: accuracy not needed b) CVA can provide clues to revisable hypotheses about unfamiliar word’s meaning
3.
Are “difficult words in natural [co-texts] usually amenable to such analysis?” a) Such words are always amenable to yielding at least some information about their meaning.

1.
Every co-text can give some clue to a word’s meaning.
2.
Co-text clues must be supplemented by reader’s prior knowledge.
a) Value of co-text depends on reader’s prior knowledge & ability to integrate them.
3.
CVA ≠ fill-in-the-blank; CVA ≠ WSD
4.
Co-text size has no arbitrary limits
5.
May need lots of co-texts before CVA can asymptotically approach a stable meaning.

Our CVA Theory (continued)
6.
A word does not have a unique meaning
7.
A word does not have a correct meaning a) A word’s “correct” (intended) meaning does not need to be known in order for reader to understand it in context
• cf. Johnson-Laird b) Even familiar/well-known words can acquire new meanings in new contexts.
c) Neologisms usually are learned from context.
8.
Some words are easier to compute meanings for than others (N < V < Adj/Adv)
9.
CVA is an efficient method for computing word meanings.
10. CVA can improve general reading comprehension

Our CVA Theory (continued)
11.

• 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.