Transcript and Presenter’s Notes

1
The Function of Background Understanding in Sentence and
Discourse Processing

• Thesis Proposal
• Raluca Budiu
• Feb 9, 2000

2
Metaphors

• Time is money.
• Individuals from all cultures use metaphors with an
  every-day basis, regardless of their degree of
  education.
• Language is filled with frozen metaphors (Adams
  apple, leg of the table, etc.)
• People understand (most) metaphors easily.

3
Mistakes

• People get some things wrong once they speak.
• Frequently people don’t notice mistakes and may
  comprehend the message conveyed
• The number of creatures of every kind did Moses take
  around the ark?
• Its difficult for individuals to not ignore mistakes.

4
Memory for Text

• People interpret new tales when it comes to past
  encounters.
• Doing that can help them recall the new tales
  better.
• Doing than means they are deform the particular details.

5
Motivation

• Metaphors
• Mistakes
• Memory for text

• Claim each one is facets of the identical cognitive
  mechanism, which
• makes up about both fallibility and sturdiness
• uses background understanding like a heuristic in
  service of the present goal.

6
Thesis Subject Comprehension

• In the semantic level, comprehension works
• bottom-up all the details available can be used
  to locate an interpretation
• top-lower the interpretation is further accustomed to
  help comprehension or recall.
• Proof a distinctive computational model in ACT-R
  (Anderson Lebiere, 1998)
• explaining and unifying phenomena from various
  domains
• satisfying numerous computational and
  empirical (i.e. fitting actual behavior data)
  constraints.

7
Behavior Data

• Metaphor understanding
• Semantic illusions
• Text memory
• Lexical Ambiguities.

8
Overview

• Thesis subject
• One for sentence comprehension
• Empirical constraints
• Computational constraints
• Summary and work plan.

9
Semantic Interpretation
Understanding a sentence locating a matching
interpretation/context without anyone’s knowledge
understanding.
10
So How Exactly Does the Model Work?
Incremental From right to left omitting
The number of
did
around the
Ark context
Farm context
Ark context
Farm context
Ark context
raise
father
take
Noah
verb
agent
verb
agent
Farm prop
Ark prop
place-oblique
place-oblique
patient
patient
creatures
creatures
ark
farm
11
Model even without the Context Priming
Read word
Extract Word Meaning
yes
no
Context?
yes
Word matches context?
no
Find context
no
Context found?
no
yes
yes
Old words match?
12
Context Priming
Different processing at the start and also at the
finish from the sentence.
ark?
The number of creatures did Noah undertake the
1. Boat or ship held to resemble that by which
Noah and the family were preserved in the Deluge
Ark story
agent
2. A repository typically in or from the
wall of the synagogue for that scrolls from the Torah
Noah
place-oblique
patient
verb
creatures
ark(1)
required
13
Model With Context Priming
Read word
Extract Context Role
Context role matches word?
yes
no
Find context
Sentence not comprehended
Context found?
no
yes
no
yes
no
Old words match?
14
Distributed Meaning Assumption
Speak very briefly
Bible char
Navigator
meaning
meaning
word
Noah
Noah
meaning
meaning
Married
Patriarch

• Meaning retrieval removing word features
• Replace word meaning with feature as unit of
  processing
• Model continues to be same.

15
Context Finding With Distributed Meanings
Show It just when you get questions
Noah
required the creatures around the ark.
word
Noah
meaning
meaning
meaning
Bible char
Married
Patriarch

Jesus context
Jesus context
Moses context
Moses context
Noah context
16
Review of the Model

• Incremental
• Trial-and-error strategy
• Combination of bottom-up and top-lower strategies
• Incomplete processing (also known as symbolic partial
  matching)
• in the word meaning level (not every features
  extracted)
• in the sentence level
• No syntactic processing thematic roles are
  inputs.

17
Overview

• Thesis Subject
• Model
• Empirical constraints
• Computational constraints
• Summary and work plan.

18
Metaphor-related Phenomena

• Results of position on metaphor understanding
  (Gerrig Healy, 1983)
• Results of metaphoric truth around the judgement and
  recall of sentences from the type Some Much like Bs
  (Glucksberg, Glidea Bookin, 1982)
• Interferences of literal and metaphoric truth on
  sentence judgements (Keysar, 1989)
• Results of context length on metaphor
  understanding (Ortony, Schallert, Reynolds
  Antos, 1978)
• Comprehension variations between differing types
  of metaphors (Gibbs, 1990 Ortony et al. 1978
  our data).

19
Metaphor Position Effects

• Metaphor-first sentences take more time to
  comprehend than metaphor-second sentences(Gerrig
  Healy, 1983).

Drops of molten silver filled

heaven

4.21s (4.23s)

Container context

Container context

Stars context

Heaven was full of

drops of molten silver

3.53s (2.84s)

Stars context

Stars context

Predictions

20
Results of Metaphoric Truth
hide

• Some roads are snakes gt Some flutes are jails
  (Glucksberg et al. 1982)
• snakes must be processed deeper in
  order for many roads are snakes to become judged as
  false.
• Congruent sentences lt incongruent sentences
  (Keysar, 1989)
• All features are equally informative within the
  congruent conditions.

RT

RT

21
Kinds of Metaphors
hide

• Literal sentences are as quickly to know as
  metaphorical sentences (Ortony et al. 1978)
• The hens clucked noisily.
• Metaphoric anaphoras are occasionally harder to
  understand than equivalent literals (Gibbs,
  1990)
• The creampuff didn’t appear for that box match.
• Will the literality of the metaphoric sentence make
  a positive change?
• The hens/women clucked/spoken noisily.

22
What Exactly Are Semantic Illusions?

• The number of creatures of every kind did Moses undertake
  the ark?
• Semantic illusions are extremely robust (Reder
  Kusbit, 1991) however, nothing could make an
  illusion.
• Good versus. bad illusions
• The number of creatures did Adam undertake the ark?

23
Semantic Illusion Datasets

• Illusion rates for negative and positive distortions
  (Ayers, Reder Anderson, 1996)
• Percent correct for negative and positive distortions in
  the gist task (Ayers et al. 1996)
• Latencies within the literal and gist task (Reder
  Kusbit, 1991)
• Processing of semantic anomalies and
  contradictions (Barton Sanford, 1993)
• When a plane crashes, where if the
  survivors be hidden? versus. Whenever a bicycle accident
  occurs where if the survivors be hidden?

24
Good versus. Bad Illusions
All amounts of distortion are considerably
not the same as each other.
25
Gist Task
Hide this
Undistorted gt Bad

• Individuals are faster and incredibly proficient at performing the
  gist task (Reder Kusbit, 1991)

26
Meaning Overlap
hide
Patriarch
Navigator
Noah
Moses
Moses
Noah
Egyptian
Married
Bible char
Adam
Adam
First man
Eve
Eden born
27
Modeling Semantic Illusions
Moses

• Model states Distorted whether it finds no
  interpretation
• Key idea meaning overlap (based on van
  Oostendorp Mul, 1990 van Oostendorp Kok,
  1990)
• Model predicts an impact of position of
  distortion within the sentence late distortions are
  harder to identify.

Noah

take

Adam

verb

agent

Ark prop

place-oblique

patient

creatures

ark

28
Memory for Text

• Prior schemas may influence text memory
  (Bartlett, 1932 Bransford Manley, 1972
  etc.)
• If your text is in line with a pre-existent
  script (paradigmatic situation/previous
  experience)
• subjects recall more propositions in the text,
• but additionally make more script-consistent intrusions
• (Owens, Bower Black, 1979).

29
Text Memory Datasets

• Recall and recognition of sentences from multiple
  episodes related or otherwise with a common setting
  (Owens et al. 1979)
• Interferences from related tales on recall and
  recognition of text (Bower, Black Turner,
  1979)
• Text recall within the presence or lack of a subject
  (Bransford Manley, 1972)
• Recall of single, related and unrelated details
  (Bradshaw and Anderson, 1982).

30
Interferences Among Related Tales

• The amount of intrusions can increase if
  subjects study more variants of the identical script
  (Bower, Black Turner, 1979)
• In the Dentists — about Bill
• In the Doctors — about Tom

31
Modeling Script Effects
Visiting-healthcare-professional script
Script Propositions
Studied Propositions
32
Elaborations
mihaib hide

• recall improved when subjects were proven the
  subject of the passage before staring at the passage
  (Bransford Manley, 1972)
• recall improved when subjects studied numerous
  related sentences about one historic figure,
  in contrast to the circumstances that they
  studied unrelated sentences about this figure or
  just one fact (Bradshaw Anderson, 1979).

33
Problems With Modeling Script Effects

• Parsing the discourse right into a unitary and coherent
  representation (solve the issue of binding)
• Text representation that enables recursive
  schemas
• Modeling various kinds of intrusions,
  especially abstract intrusions

• Studied
  Intruded
• Bill compensated the balance.
  Tom compensated the balance.
• The nurse x-rayed Bills
  The nurse checked Toms
• teeth.
  bloodstream pressure.

34
Lexical Ambiguity Resolution

• While not created for data out of this domain,
  our model makes strong predictions about
  ambiguity resolution.
• Does context influence meaning access to have an
  ambiguous word?
• Possible answer both meanings are activated, but
  activation depends additively on context
  and individual meaning frequency (Tabossi, 1988
  Duffy, Morris Rayner, 1988 Rayner Duffy,
  1986 Rayner Frazier, 1989 Lucas, 1999).

35
Lexical Ambiguity Datasets

• Gaze duration on balanced and unbalanced
  homophones (Duffy et al. 1988)
• Mean studying time per character within the
  disambiguation region (Duffy et al. 1988)

36
Lexical Ambiguity A Watch Movement Study (Duffy
et al. 1988)
Mention controls hide
Disambiguation-before
Disambiguation-after

• Since it was stored on the rear of a higher shelf,
  the pitcher (whiskey) was frequently forgotten.

Obviously the pitcher (whiskey) was frequently
forgotten since it was stored on the rear of a
high shelf.

Balanced

When she finally offered it to her visitors, the
port (soup) was successful.

Yesterday the main harbour (soup) was successful,
when she finally offered it to her visitors.

Unbalanced

Context always supports subordinate meaning for
unbalanced words.

37
Gaze Durations on Homophones

• Duffy et al. (1988) manipulated position of
  disambiguating region and relative frequency of
  the homophones meanings
• Disambiguating region before/following the homophone
• Homophone might be balanced (pitcher) or
  unbalanced (port)

38
Gaze Duration on Homophones

• Occasions more than controls reflect multiple
  access.
• Occasions equal with controls reflect selective
  access.

39
Time Allocated to Disambiguating Region
mihaib hide
40
Fitting the information

• Disambiguation-after
• no context priming
• individual meaning activation is proportional
  with meaning frequency (ACT-R assumption)
• ACT-R is serial (no multiple access), but close
  competitors can slow lower retrieval (tentative
  ACT-R assumption).
• Disambiguation-before
• context priming context is definitely an extra supply of
  activation
• When the wrong meaning is much more frequent, context
  priming might not be enough.

41
Overview

• Thesis Subject
• Model
• Empirical constraints
• Computational constraints
• Summary and work plan.

42
Computational Constraints

• Realistic reaction occasions
• Integration with background understanding
• Permitting errors from the syntactic processor
  (i.e. wrong thematic roles).

43
Syntactic Ambiguity Like a Computational Constraint

• Garden path effects happen to be largely
  documented within the literature
• The horse raced beyond the barn fell
• The cop arrested through the detective was responsible for
  taking bribes.

Solution thematic roles as meaning features
later overlooked.

44
Summary

• Language comprehension theory to become embodied in
  a distinctive ACT-R model
• Semantic instead of syntactic degree of
  processing (no parser)
• The idea should satisfy
• Computational constraints
• Realistic reaction occasions
• Integration with background understanding
• Syntactic ambiguity.
• Empirical constraints
• Metaphor understanding
• Semantic illusions
• Lexical ambiguity
• Memory for text script effects and elaborations.

45
Empirical Constraints

• Metaphor understanding
• Results of position on metaphor understanding
  (Gerrig Healy, 1983)
• Results of metaphoric truth around the judgement and
  recall of sentences from the type Some Much like Bs
  (Glucksberg et al. 1982)
• Interferences of literal and metaphoric truth on
  sentence judgements (Keysar, 1989)
• Results of context length on metaphor
  understanding (Ortony et al. 1978)
• Comprehension variations between differing types
  of metaphors (Gibbs, 1990 Ortony et al. 1979
  our data).

46
Empirical Constraints (contd.)

• Semantic illusions
• Illusion rates for negative and positive distortions in
  the literal and gist tasks (Ayers et al. 1996)
• Latencies within the literal and gist task (Reder
  Kusbit, 1991)
• Processing of semantic anomalies and
  contradictions (Barton Sanford, 1993).
• Lexical ambiguity
• Gaze duration on balanced and unbalanced
  homophones (Duffy et al. 1988)
• Mean studying time per character within the
  disambiguation region (Duffy et al. 1988)

47
Empirical Constraints (contd.)

• Memory for text (script effects and
  elaborations)
• Recall and recognition of sentences from multiple
  episodes related or otherwise with a common setting
  (Owens et al. 1979)
• Interferences from related tales on recall and
  recognition of text (Bower et al. 1979)
• Text recall within the presence or lack of a subject
  (Bransford Manley, 1972)
• Recall of single, related and unrelated details
  (Bradshaw and Anderson, 1982).

48
Model Validation

• Collect new empirical data to validate side
  effects or any other predictions from the model, not
  taught in previous listing of empirical
  phenomena
• E.g. position effects for Moses illusion.
• Test drive it on other teams of data (for the similar
  phenomena) compared to ones it’s been designed for
  to prevent overfitting.

49
Work Plan
Garden path
Lexical ambiguity
Text memory
Semantic illusions
Metaphor
20
10
15
30
25

• Modeling and parameter fitting
• Data collection metaphors and semantic
  illusions
• The model continues to have to resolve the greater difficult
  problems of discourse representation.

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