cva-nacap.ppt

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cva-nacap.ppt

• 20060620

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Contextual Vocabulary Acquisitionas
Computational Philosophyand as Philosophical
Computation

• William J. Rapaport
• Department of Computer Science Engineering,
• Department of Philosophy,
• and Center for Cognitive Science
• rapaport_at_cse.buffalo.edu
• http//www.cse.buffalo.edu/rapaport

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Computation Philosophy

• Computational philosophy
• Application of computational (i.e., algorithmic)
  solutionsto philosophical problems
• Use of SNePS KRR belief-revision systemto
  solve problems in representation of fictional
  entities
• Rapaport 1991 Rapaport Shapiro 1995, 1999
• CVA
• Philosophical computation
• Application of philosophy to CS problems
• Use of Castañedas theory of quasi-indexicalsto
  solve problems in knowledge representation
• Maida Shapiro 1982 Rapaport 1986 Rapaport,
  Shapiro, Wiebe 1997
• CVA
• (more later)

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Contextual Vocabulary Acquisition

• CVA active, deliberate acquisition of a meaning
  for a word in a text by reasoning from
  context
• CVA what you do when
• Youre reading
• You come to an unfamiliar word
• Its important for understanding the passage
• No ones around to ask
• Dictionary doesnt help
• No dictionary
• Too lazy to look it up -)
• Word not in dictionary
• Definition of no use
• Too hard
• Inappropriate
• So, you figure out a meaning for the word from
  context
• figure out compute (infer) a hypothesis
  about what the word might mean in that
  text
• context ??

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What does brachet mean?

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What Does Brachet Mean?(From Malorys Morte
DArthur page in brackets)

• 1. There came a white hart running into the
  hall with a white brachet next to him, and thirty
  couples of black hounds came running after them.
  66
• As the hart went by the sideboard,the white
  brachet bit him. 66
• The knight arose, took up the brachet androde
  away with the brachet. 66
• A lady came in and cried aloud to King
  Arthur,Sire, the brachet is mine. 66
• There was the white brachet which bayed at him
  fast. 72
• 18. The hart lay dead a brachet was biting on
  his throat,and other hounds came behind. 86

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CVA as Computational Philosophy

• Origin of project
• Rapaport, How to Make the World Fit Our
  Language (1981)
• Neo-Meinongian theory of a words meaning for a
  person as the set of contexts in which person has
  heard or seen word.
• Could that notion be made precise?
• Semantic-network theory offered a computational
  tool
• Later, learned that computational linguists,
  reading educators, L2 educators, psychologists,
  were all interested in this
• A really interdisciplinary cognitive-science
  problem
• Developed into Karen Ehrlichs CS Ph.D.
  dissertation (1995)

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What Is the Context for CVA?

• context ? textual context
• surrounding words co-text of word
• context wide context
• internalized co-text
• readers interpretive mental model of textual
  co-text
• integrated via belief revision
• infer new beliefs from internalized co-text
  prior knowledge
• remove inconsistent beliefs
• with readers prior knowledge
• world knowledge
• language knowledge
• previous hypotheses about words meaning
• but not including external sources (dictionary,
  humans)
• ? Context for CVA is in readers mind, not in
  the text

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Meaning of Meaning

• a meaning for a word vs. the meaning of a
  word
• the ? single, correct meaning
• of ? meaning belongs to word
• a ? many possible meanings depending on
  textual context,
• readers prior knowledge, etc.
• for ? reader hypothesizes meaning from
  context, gives it to word

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The meaning of things lies not in themselves but
in our attitudes toward them.- Antoine de
Saint-Exupéry,Wisdom of the Sands (1948)
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The meaning of things lies not in themselves but
in our attitudes toward them.
words
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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
T1
internalization
PK1 PK2 PK3 PK4
I(T1)
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B-R Integrated KB
Text
T1
internalization
PK1 PK2 PK3 PK4
I(T1)
inference
P5
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B-R Integrated KB
Text
T1
internalization
PK1 PK2 PK3 PK4
I(T1)
T2
inference
P5
I(T2)
P6
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B-R Integrated KB
Text
T1
internalization
PK1 PK2 PK3 PK4
I(T1)
T2
inference
T3
P5
I(T2)
P6
I(T3)
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B-R Integrated KB
Text
T1
internalization
PK1 PK2 PK3 PK4
I(T1)
T2
inference
T3
P5
I(T2)
P6
I(T3)
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Note All contextual reasoning is done in this
context
B-R Integrated KB
Text
T1
internalization
PK1 PK2 PK3 PK4
P7
I(T1)
T2
inference
T3
P5
I(T2)
P6
I(T3)
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Note All contextual reasoning is done in this
context
B-R Integrated KB (the readers mind)
Text
T1
internalization
PK1 PK2 PK3 PK4
P7
I(T1)
T2
inference
T3
P5
I(T2)
P6
I(T3)
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Overview of CVA Project

• Background
• People do incidental CVA
• Possibly best explanation of how we learn
  vocabulary
• Given of words high-school grad knows (45K),
  of years to learn them (18) 2.5K words/year
• But only taught 10 in 12 school years
• Students are taught deliberate CVAin order to
  improve their vocabulary
• CVA project From Algorithm to Curriculum
• Implemented computational theory of how tofigure
  out (compute) a meaning for an unfamiliar
  wordfrom wide context
• Convert algorithms to an improved, teachable
  curriculum

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Computational CVA

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

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Cassie learns what brachet meansBackground
info about harts, animals, King Arthur, etc.No
info about brachetsInput formal-language
(SNePS) version of simplified EnglishA hart
runs into King Arthurs hall. In the story, B12
is a hart. In the story, B13 is a hall. In
the story, B13 is King Arthurs. In the story,
B12 runs into B13.A white brachet is next to
the hart. In the story, B14 is a brachet. In
the story, B14 has the property white.
Therefore, brachets are physical objects.
(deduced while reading PK Cassie believes
that only physical objects have color)
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--gt (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
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A hart runs into King Arthurs hall.A white
brachet is next to the hart.The brachet bites
the harts buttock. PK Only animals
bite--gt (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 Arthurs hall. A white
brachet is next to the hart. The brachet bites
the harts buttock. The knight picks up the
brachet. The knight carries the brachet. PK
Only small things can be picked up/carried --gt
(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 Arthurs hall.A white
brachet is next to the hart.The brachet bites
the harts buttock.The knight picks up the
brachet.The knight carries the brachet.The lady
says that she wants the brachet. PK Only
valuable things are wanted--gt (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 Arthurs hall.A white
  brachet is next to the hart.The brachet bites
  the harts buttock.The knight picks up the
  brachet.The knight carries the brachet.The lady
  says that she wants the brachet.
• The brachet bays at Sir Tor. PK Only hunting
  dogs bay
• --gt (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

• Cassies behavior ? human protocols
• Cassies definition ? OEDs definition
• A brachet is a kind of hound which hunts by
  scent

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Fragment of readers prior knowledge m3 In
real life, white is a color
Member(Lex(white),Lex(color),LIFE) m6 In
real life, harts are deer
AKO(Lex(hart),Lex(deer),LIFE) m8 In real
life, deer are mammals
AKO(Lex(deer),Lex(mammal),LIFE) m11 In real
life, halls are buildings
AKO(Lex(hall),Lex(building),LIFE) m12 In real
life, b1 is named King Arthur
Name(b1,King Arthur,LIFE) m14 In real life,
b1 is a king Isa(ISA,b1,Lex(king),LIFE)
(etc.)
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m16 if v3 has property v2 v2 is a color v3
? v1 then v1 is a class of physical
objects all(x,y,z)(Is1(z,y),Member1(y,lex(color))
,Member1(z,x) gt
AKO1(x,lex(physical object)))
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Reading the story m17 In the story, b2 is a
hart ISA(b2,lex(hart),STORY) m24 In
the story, the hart runs into b3 Does(b2,into(b3,
lex(run)),STORY) (b3 is King Arthurs hall) not
shown (harts are deer) not shown
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A fragment of the entire network showing the
readers mental context consisting of prior
knowledge, the story, inferences. The
definition algorithm searches this entire
network,abstracts parts of it, produces a
hypothesized meaning for brachet.
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Noun Algorithm

• Generate initial hypothesis by syntactic
  manipulation
• Algebra Solve an equation for unknown value X
• Syntax Solve a sentence for unknown word X
• A white brachet (X) is next to the hart? X (a
  brachet) is something that is next to the hart
  and that can be white
• I.e., define node X in terms of immediately
  connected nodes
• Then find or infer from wide context
• Basic-level class memberships (e.g., dog,
  rather than animal)
• else most-specific-level class memberships
• else names of individuals
• Properties of Xs (else, of individual Xs) (e.g.,
  size, color, )
• Structure of Xs (else ) (part-whole, physical
  structure)
• Acts that Xs perform (else ) or that can be done
  to/with Xs
• Agents that do things to/with Xs
• or to whom things can be done with Xs
• or that own Xs
• Possible synonyms, antonyms
• I.e., define word X in terms of some (but not
  all) other connected nodes

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Verb Algorithm

• Generate initial hypothesis by syntactic/algebraic
  manipulation
• Then find or infer from wide context
• Class membership (e.g., Conceptual Dependency)
• What kind of act is X-ing (e.g., walking is a
  kind of moving)
• What kinds of acts are X-ings (e.g., sauntering
  is a kind of walking)
• Properties/manners of X-ing (e.g., moving by
  foot, slow walking)
• Transitivity/subcategorization information
• Return class membership of agent, object,
  indirect object, instrument
• Possible synonyms, antonyms
• Causes effects
• Also preliminary work on adjective algorithm

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Belief Revision

• To revise definitions of words used
  inconsistently with current meaning hypothesis
• SNeBR (ATMS Martins Shapiro 1988, Johnson
  2006)
• If inference leads to a contradiction, then
• SNeBR asks user to remove culprit(s)
• automatically removes consequences inferred
  from culprit

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Revision Expansion

• Removal revision being automated via SNePSwD by
  ranking all propositions with kn_cat
• most intrinsic info re language fundamental
  background info
• certain (before is transitive)
• story info in text (King Lot rode
  to town)
• life background info w/o variables or
  inference
• (dogs are animals)
• story-comp info inferred from text (King
  Lot is a king, rode on a horse)
• life-rule.1 everyday commonsense
  background info
• (BearsLiveYoung(x) ? Mammal(x))
• life-rule.2 specialized background info
• (x smites y ? x kills y by
  hitting y)
• least
• certain questionable already-revised
  life-rule.2 not part of input

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Belief Revision smite

• Misunderstood word
• Initially believe that smite meanskill by
  hitting
• Read King Lot smote down King Arthur
• Infer that King Arthur is dead
• Then read King Arthur drew his sword Excalibur
• Contradiction!
• Weaken definition to hit and possibly kill
• Then read more passages in which smiting ?
  killing
• Hypothesize that smite means hit

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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
• KAs 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

• Well-entrenched word
• Believe dress means put clothes on
• Commonsense belief
• Spears dont wear clothing
• used in new sense
• Read King Claudius dressed his spear
• Infer that spear wears clothing
• Contradiction!
• Modify definition to put clothes on OR
  something else
• Read King Arthur dressed his troops before
  battle
• Infer that dress means put clothes on OR
  prepare for battle
• Eventually Induce more general definition
• prepare (for the day, for battle, for eating)

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Belief Revision dress

• additive revision
• Background info includes
• dresses(x,y) ? ?zclothing(z) wears(y,z)
• Spears dont wear clothing (both
  kn_catlife.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 Claudiuss spear wears
  clothing.
• Q2 What wears clothing?
• SNeBR is invoked
• KCs spear wears clothing inconsistent with (2).
• (1) replaced dresses(x,y) ? ?zclothing(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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A Computational Theory of CVA

• A word does not have a unique meaning.
• A word does not have a correct meaning.
• Authors intended meaning for word doesnt need
  to be known by readerin order for reader to
  understand word in context
• Even familiar/well-known words can acquire new
  meanings in new contexts.
• Neologisms are usually learned only from context
• Every co-text can give some clue to a meaning for
  a word.
• Generate initial hypothesis via
  syntactic/algebraic manipulation
• But co-text must be integrated with readers
  prior knowledge
• Large co-text large PK ? more clues
• Lots of occurrences of word allow asymptotic
  approach to stable meaning hypothesis
• CVA is computable
• CVA is open-ended, hypothesis generation.
• CVA ? guess missing word (cloze) ? CVA ?
  word-sense disambiguation
• Some words are easier to compute meanings for
  than others (N lt V lt Adj/Adv)
• CVA can improve general reading comprehension
  (through active reasoning)
• CVA can should be taught in schools

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From Algorithm to Curriculum

• State of the art in vocabulary learning from
  context
• Mauser 1984 context definition!
• Clarke Nation 1980 a strategy (algorithm?)
• Determine part of speech of word
• Look at grammatical context
• Who does what to whom?
• Look at surrounding textual context
• Search for clues (as we do)
• Guess the word check your guess

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CVA From Algorithm to Curriculum

• guess the word
• then a miracle occurs
• Surely, computer scientists
• can be more explicit!
• And so should teachers!

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From Algorithm to Curriculum (contd)

• We have explicit, GOF (symbolic) AI theory of how
  to do CVA
• ? Teachable!
• Goal
• Not teach people to think like computers
• But explicate computable teachable
  methods to hypothesize word meanings from
  context
• AI as computational psychology
• Devise computer programs that faithfully
  simulate(human) cognition
• Can tell us something about (human) mind
• Joint work with Michael Kibby (UB Reading Clinic)
• We are teaching a machine, to see if what we
  learn in teaching it can help us teach students
  better

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CVA as Computational Philosophy Philosophical
Computation

• CVA holistic semantic theories
• Semantic networks
• Meaning of a node is its location in the entire
  network
• Holism
• Meaning of a word is its relationships to all
  other words in the language
• Problems (Fodor Lepore)
• No 2 people ever share a belief
• No 2 people ever mean the same thing
• No 1 person ever means the same thing at
  different times
• No one can ever change his/her mind
• Nothing can be contradicted
• Nothing can be translated
• CVA offers principled way to restrict entire
  networkto a useful subnetwork
• That subnetwork can be shared across people,
  individuals, languages,
• Can also account for language/concept change
• Via dynamic/incremental semantics

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CVA as Computational Philosophy Philosophical
Computation (contd)

• CVA and the Chinese Room
• How would Searle-in-the-Room figure out the
  meaning of an unknown squiggle?
• By CVA techniques!
• Searles CR argument from semantics
• Computer programs are purely syntactic
• Cognition is semantic
• Syntax alone does not suffice for semantics
• ? No purely syntactic computer program can
  exhibit semantic cognition
• Syntactic Semantics (Rapaport 1985ff)
• Syntax does suffice for the kind of semantics
  needed for NLU in the CR
• All inputlinguistic, perceptual, etc.is encoded
  in a single network(or in a single, real
  neural network the brain!)
• Relationsincluding semantic onesamong nodes of
  such a networkare manipulated syntactically
• Hence computationally (CVA helps make this
  precise)

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Summary

• Contextual Vocabulary Acquisition project is
• Computational philosophy
• And computational psychology!
• Philosophical computation
• With applications to
• Computational linguistics
• Reading education