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Master thesis proposal presentation ppt download

Transcript and Presenter’s Notes

1
The Role of Background Knowledge in Sentence and
Discourse Processing

  • Thesis Proposal
  • Raluca Budiu
  • February 9, 2000

2
Metaphors

  • Time is money.
  • People from all cultures use metaphors on an
    every-day basis, irrespective of their level of
    education.
  • Language is full of frozen metaphors (Adams
    apple, leg of a table, etc.)
  • People understand (most) metaphors easily.

3
Mistakes

  • People make mistakes when they speak.
  • Often people do not notice mistakes and can
    understand the message communicated
  • How many animals of each kind did Moses take
    on the ark?
  • Its hard for people not to ignore mistakes.

4
Memory for Text

  • People interpret new stories in terms of past
    experiences.
  • Doing that helps them remember the new stories
    better.
  • Doing than makes them deform the actual facts.

5
Motivation

  • Metaphors
  • Mistakes
  • Memory for text
  • Claim all are facets of the same cognitive
    mechanism, which
  • accounts for both fallibility and robustness
  • uses background knowledge as a heuristic in
    service of the current goal.

6
Thesis Topic Comprehension

  • At the semantic level, comprehension works
  • bottom-up all the information available is used
    to find an interpretation
  • top-down the interpretation is further used to
    help comprehension or recall.
  • Proof a unique computational model in ACT-R
    (Anderson Lebiere, 1998)
  • explaining and unifying phenomena from various
    domains
  • satisfying a number of computational and
    empirical (i.e. fitting actual behavioral data)
    constraints.

7
Behavioral Data

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

8
Overview

  • Thesis topic
  • A model for sentence comprehension
  • Empirical constraints
  • Computational constraints
  • Summary and work plan.

Master thesis proposal presentation ppt download the     end of the sentence



9
Semantic Interpretation
Understanding a sentence finding a matching
interpretation/context in the background
knowledge.
10
How Does the Model Work?
Incremental From left to right omitting
How many
did
on 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
animals
animals
ark
farm
11
Model in the Absence of 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 beginning and at the
end of the sentence.
ark?
How many animals did Noah take on the
1. Boat or ship held to resemble that in which
Noah and his family were preserved from the Deluge
Ark story
agent
2. A repository traditionally in or against the
wall of a synagogue for the scrolls of the Torah
Noah
place-oblique
patient
verb
animals
ark(1)
took
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 extracting word features
  • Replace word meaning with feature as unit of
    processing
  • Model remains the same.

15
Context Finding With Distributed Meanings
Show It only if you get questions
Noah
took the animals on the ark.
word
Noah
meaning
meaning
meaning
Bible char
Married
Patriarch

Master thesis proposal presentation ppt download 29    Text Memory Datasets

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

  • Incremental
  • Trial-and-error strategy
  • Mixture of bottom-up and top-down strategies
  • Incomplete processing (aka symbolic partial
    matching)
  • at the word meaning level (not all features
    extracted)
  • at the sentence level
  • No syntactic processing thematic roles are
    inputs.

17
Overview

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

18
Metaphor-related Phenomena

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

19
Metaphor Position Effects

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

Drops of molten silver filled

the sky

4.21s (4.23s)

Container context

Container context

Stars context

The sky was filled with

drops of molten silver

3.53s (2.84s)

Stars context

Stars context

Predictions

20
Effects of Metaphoric Truth
hide

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

RT

RT

21
Types of Metaphors
hide

  • Literal sentences are as fast to understand as
    metaphorical sentences (Ortony et al. 1978)
  • The hens clucked noisily.
  • Metaphoric anaphoras are sometimes harder to
    understand than equivalent literals (Gibbs,
    1990)
  • The creampuff did not show up for the box match.
  • Does the literality of a metaphoric sentence make
    a difference?
  • The hens/women clucked/talked noisily.

22
What Are Semantic Illusions?

  • How many animals of each kind did Moses take on
    the ark?
  • Semantic illusions are very robust (Reder
    Kusbit, 1991) however, not anything can make an
    illusion.
  • Good vs. bad illusions
  • How many animals did Adam take on the ark?

23
Semantic Illusion Datasets

  • Illusion rates for good and bad distortions
    (Ayers, Reder Anderson, 1996)
  • Percent correct for good and bad distortions in
    the gist task (Ayers et al. 1996)
  • Latencies in the literal and gist task (Reder
    Kusbit, 1991)
  • Processing of semantic anomalies and
    contradictions (Barton Sanford, 1993)
  • When an aircraft crashes, where should the
    survivors be buried? vs. When a bicycle accident
    occurs where should the survivors be buried?

24
Good vs. Bad Illusions
All levels of distortion are significantly
different from one another.
25
Gist Task
Hide this
Undistorted gt Bad

  • People are faster and very good 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 says Distorted if it finds no
    interpretation
  • Key idea meaning overlap (supported by van
    Oostendorp Mul, 1990 van Oostendorp Kok,
    1990)
  • Model predicts an effect of position of
    distortion in the sentence late distortions are
    harder to detect.

Noah

take

Adam

verb

agent

Ark prop

place-oblique

patient

animals

ark

28
Memory for Text

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

29
Text Memory Datasets

  • Recall and recognition of sentences from multiple
    episodes related or not by a common setting
    (Owens et al. 1979)
  • Interferences from related stories on recall and
    recognition of text (Bower, Black Turner,
    1979)
  • Text recall in the presence or absence of a topic
    (Bransford Johnson, 1972)
  • Recall of single, related and unrelated facts
    (Bradshaw and Anderson, 1982).

30
Interferences Among Related Stories

  • The number of intrusions can increase if
    subjects study more variants of the same script
    (Bower, Black Turner, 1979)
  • At the Dentists — about Bill
  • At 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 shown the
    topic of a passage before studying the passage
    (Bransford Johnson, 1972)
  • recall improved when subjects studied a number of
    related sentences about one historical figure,
    compared with the conditions in which they
    studied unrelated sentences about that figure or
    a single fact (Bradshaw Anderson, 1979).

33
Difficulties With Modeling Script Effects

  • Parsing the discourse into a unitary and coherent
    representation (solve the problem of binding)
  • Text representation that allows recursive
    schemas
  • Modeling different types of intrusions,
    especially abstract intrusions
  • Studied
    Intruded
  • Bill paid the bill.
    Tom paid the bill.
  • The nurse x-rayed Bills
    The nurse checked Toms
  • teeth.
    blood pressure.

34
Lexical Ambiguity Resolution

  • Although not designed for data from this domain,
    our model makes strong predictions about
    ambiguity resolution.
  • Does context influence meaning access for an
    ambiguous word?
  • Possible answer both meanings are activated, but
    activation depends additively on both 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 reading time per character in the
    disambiguation region (Duffy et al. 1988)

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

  • Because it was kept on the back of a high shelf,
    the pitcher (whiskey) was often forgotten.

Of course the pitcher (whiskey) was often
forgotten because it was kept on the back of a
high shelf.

Balanced

When she finally served it to her guests, the
port (soup) was a great success.

Last night the port (soup) was a great success,
when she finally served it to her guests.

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/after the homophone
  • Homophone could be balanced (pitcher) or
    unbalanced (port)

38
Gaze Duration on Homophones

  • Times longer than controls reflect multiple
    access.
  • Times equal with controls reflect selective
    access.

39
Time Spent on Disambiguating Region
mihaib hide
40
Fitting the Data

  • 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 down retrieval (tentative
    ACT-R assumption).
  • Disambiguation-before
  • context priming context is an extra source of
    activation
  • If the wrong meaning is more frequent, context
    priming may not be enough.

41
Overview

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

42
Computational Constraints

  • Realistic reaction times
  • Integration with background knowledge
  • Allowing for errors of the syntactic processor
    (i.e. wrong thematic roles).

43
Syntactic Ambiguity As a Computational Constraint

  • Garden path effects have been largely
    documented in the literature
  • The horse raced past the barn fell
  • The cop arrested by the detective was guilty of
    taking bribes.

Solution thematic roles as meaning features
later omitted.

44
Summary

  • Language comprehension theory to be embodied in
    a unique ACT-R model
  • Semantic rather than syntactic level of
    processing (no parser)
  • The theory should satisfy
  • Computational constraints
  • Realistic reaction times
  • Integration with background knowledge
  • Syntactic ambiguity.
  • Empirical constraints
  • Metaphor understanding
  • Semantic illusions
  • Lexical ambiguity
  • Memory for text script effects and elaborations.

45
Empirical Constraints

  • Metaphor understanding
  • Effects of position on metaphor understanding
    (Gerrig Healy, 1983)
  • Effects of metaphoric truth on the judgement and
    recall of sentences of the type Some As are Bs
    (Glucksberg et al. 1982)
  • Interferences of literal and metaphoric truth on
    sentence judgements (Keysar, 1989)
  • Effects of context length on metaphor
    understanding (Ortony et al. 1978)
  • Comprehension differences between different types
    of metaphors (Gibbs, 1990 Ortony et al. 1979
    our data).

46
Empirical Constraints (contd.)

  • Semantic illusions
  • Illusion rates for good and bad distortions in
    the literal and gist tasks (Ayers et al. 1996)
  • Latencies in 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 reading time per character in 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 not by a common setting
    (Owens et al. 1979)
  • Interferences from related stories on recall and
    recognition of text (Bower et al. 1979)
  • Text recall in the presence or absence of a topic
    (Bransford Johnson, 1972)
  • Recall of single, related and unrelated facts
    (Bradshaw and Anderson, 1982).

48
Model Validation

  • Collect new empirical data to validate side
    effects or other predictions of the model, not
    covered by the previous list of empirical
    phenomena
  • E.g. position effects for Moses illusion.
  • Test it on other sets of data (for the same
    phenomena) than the ones it has been built for
    in order to avoid 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 still has to solve the more difficult
    problems of discourse representation.

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