Contextual Vocabulary Acquisition: From Algorithm to Curriculum

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(No Transcript)
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Contextual Vocabulary AcquisitionFrom Algorithm
to Curriculum

• William J. Rapaport
• Department of Computer Science Engineering
• Department of Philosophy
• Center for Cognitive Science
• Michael W. Kibby
• Department of Learning Instruction
• Center for Literacy Reading Instruction
• State University of New York at Buffalo
• NSF ROLE Grant REC-0106338

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Definition of CVA

• Contextual Vocabulary Acquisition def
• the acquisition of word meanings from text
• incidental
• deliberate
• by reasoning about
• contextual cues
• background knowledge
• Including hypotheses from prior encounters (if
  any) with the word
• without external sources of help
• No dictionaries
• No people

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Project Goals

• Develop implement computational theory of CVA
  based on verbal protocols (case studies)
• Translate algorithms into a curriculum
• To improve CVA and reading comprehension in
  science, technology, engineering, math (STEM)
• Use new case studies, based on the curriculum,
  to improve both algorithms curriculum

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People Do Incidental CVA

• Know more words than explicitly taught
• Average high-school senior knows 40K words
• ? learned 3K words/school-year (over 12 yrs.)
• But only taught a few hundred/school-year
• ? Most word meanings learned from context
• incidentally (unconsciously)
• How?

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People Also Do Deliberate CVA

• Youre 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/learn/acquire
• its meaning from
• its context
• your background knowledge
• How?
• guess? derive? infer? deduce? educe? construct?
  predict?

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What does brachet mean?

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(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 and rode
  away with the brachet. 66
• A lady came in and cried aloud to King Arthur,
  Sire, the brachet is mine. 66
• There was the white brachet which bayed at him
  fast. 72
• 18. The hart lay dead a brachet was biting on
  his throat, and other hounds came behind. 86

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Computational cognitive theory of how to learn
word meanings

• From context
• I.e., text grammatical info readers 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 words meaning
• Revisable each time word is seen

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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 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.--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. --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.--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. background
  knowledge 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

• Systems behavior ? human protocols
• Systems definition ? OEDs 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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Motivations Applications

• Part of cognitive-science projects
• Narrative text understanding
• Syntactic semantics (contra Searles Chinese-Room
  Argument)
• Computational applications
• Information extraction
• Autonomous intelligent agents
• There can be no complete lexicon
• Agent/info-extraction system shouldnt have to
  stop to ask questions
• Other applications
• L1 L2 acquisition research
• Computational lexicography
• Education improve reading comprehension

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State of the Art

• Vocabulary Learning
• Some dubious contributions
• Useless algorithms
• Contexts that include definition
• Useful contribution
• (good) readers 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

• Granger 77 Foul-Up
• Based on Schanks theory of scripts
• Our system not restricted to scripts
• Zernik 87 self-extending phrasal lexicon
• Uses human informant
• Ours system is really self-extending
• Hastings 94 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 81,87
• Application of Winstons AI arch learning
  theory
• (Good) readers 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

• Sternberg et al. 83,87
• Cues to look for ( slots for frame)
• Spatiotemporal cues
• Value cues
• Properties
• Functions
• Cause/enablement information
• Class memberships
• Synonyms/antonyms
• Johnson-Laird 87
• Word understanding ? definition
• Definitions arent stored

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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.
• To acquire new words from context
• Distinguish relevant/irrelevant information
• Selectively combine relevant information
• Compare this information with previous beliefs

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State of the Art Vocabulary Learning (II)

• Some dubious contributions
• Mueser 84 Practicing Vocabulary in Context
• BUT context definition !!
• Clarke Nation 80 a strategy (algorithm?)
• Look at word context determine POS
• Look at grammatical context
• E.g., what does what?
• Look at wider context
• 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
• Surely,
• we computer scientists
• can be more explicit!

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CVA From algorithm to curriculum

• Treat guess as a procedure call
• 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 of STEM texts
• 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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Question (objection)

• 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 pure, clean /? new dishes are
  chaste
• Most words learned via incidental CVA,
• not via dictionaries

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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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Implementation

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

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How It Works

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

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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 Ving
• 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
• 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 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 2-stage subtractive
  revision
• Bkgd info 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

• additive revision
• Bkgd 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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Using SNePS Networks

• See handout

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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 ConvertingAlgorithm 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 dont 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 ones 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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CVA Geography (? STEM)

• Use texts w/ unknown geographic terms
• estuary
• proximity (IGERTs own Valerie Raybold Yakich
  ?)
• L1 acquisition of spatial terms
• Childrens concepts ? adult concepts
• L2 acquisition of spatial terms
• L2 spatial concepts ? L1 spatial concepts
• Especially spatial prepositions

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Conclusion

• Developing a computational theory of CVA,
• which can become
• a useful educational technique for improving
  STEM vocabulary and reading comprehension
• a model of the scientific method
• a useful tool for learning on ones own.

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Participants
Santosh Basapur (IE) Adam Lammert (Vasser/ugrad) Taha Suglatwala (CSE)
Aishwarya Bhave (CSE) Amanda MacRae (LAI) Matthew Sweeney (ENG/CSE/ugrad)
Marc Broklawski (CSE) Brian Morgan (LAI) Matthew Watkins (CEN)
Chien-chih Chi (PHI) Scott Napieralski (CSE) Karen Wieland (LAI)
Justin Del Vecchio (CSE) Vikranth Rao (CSE/ugrad) Yulai Xie (CSE)
Karen Ehrlich (Fredonia) Laurie Schimpf (LAI) Valerie Raybold Yakich (GEO)
Jeffrey Galko (PHI) Ashwin Shah (CSE) SNeRG members
Christopher Garver (CSE) Stuart C. Shapiro (UB/CSE) ( new students, Spring 2003)
Paul Gestwicki (CSE) Anuroopa Shenoy (CSE)
Kazuhiro Kawachi (LIN) Rajeev Sood (CSE) ( supported on NSF grant)
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Web Page

• http//www.cse.buffalo.edu/rapaport/cva.html