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Thesis proposal presentation ppt slides

Thesis proposal presentation ppt slides Behavioral Data         Metaphor understanding         Semantic

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

Thesis proposal presentation ppt slides van Oostendorp Kok

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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