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Thesis Proposal
Raluca Budiu
February 9, 2000
The Role of Background Knowledge in Sentence and Discourse Processing
02/ 09/ 2000 Thesis proposal --- Raluca Budiu2
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 (Adam’s apple, leg of a table, etc.)
People understand (most) metaphors easily.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu3
“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?
It’s hard for people not to ignore mistakes.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu4
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.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu5
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.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu6
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.
Thesis Topic: Comprehension
02/ 09/ 2000 Thesis proposal --- Raluca Budiu8
Overview
Thesis topic; A model for sentence comprehension; Empirical constraints; Computational constraints; Summary and work plan.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu9
Semantic Interpretation
ModelSemantic
Background
Words+
thematicroles
knowledge
interpretation
Understanding a sentence =finding a matching interpretation/context in the background knowledge.
take
arkanimals
Noah
Ark prop
agent verb
place-obliquepatient
02/ 09/ 2000 Thesis proposal --- Raluca Budiu10
How Does the Model Work?
Ark context
arkHow many did
Farm context
Ark context
Ark context
animals Noah take
Farm context
raise
farmanimals
father
Farm prop
agent verb
place-obliquepatient
on the
take
arkanimals
Noah
Ark prop
agent verb
place-obliquepatient
Incremental
From left to right
omitting
Incremental
From left to right
omitting
02/ 09/ 2000 Thesis proposal --- Raluca Budiu11
Model in the Absence of Context Priming
Context found?
Read word
Extract Word Meaning
Context?
Word matches context?
Find context
Old words match?
yes
no
yesno
nono
yesyes
= here the model may omit to check all the previous words
02/ 09/ 2000 Thesis proposal --- Raluca Budiu12
Context Priming
How many animals did Noah take on the ark?
1. Boat or ship held to resemble that in which Noah and his family were preserved from the Deluge
2. A repository traditionally in or against the wall ofa synagogue for the scrolls of the Torah
Ark story
Noah
animals
took
ark(1)
agent
verb
place-oblique
patie
nt
Different processing at the beginning and at the end of the sentence.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu13
Model With Context Priming
Read word
Extract Context Role
Context role
matches word?
Find context
Old words match?
yesno
no
noyes
yes
Context found?
Sentence notcomprehended
= here the model may omit to check all the previous words
no
02/ 09/ 2000 Thesis proposal --- Raluca Budiu14
Distributed Meaning Assumption
Bible char Navigator
MarriedPatriarch
Noah “Noah”
meaning
word
meaning
meaningmeaning
• Meaning retrieval = extracting word features;• Replace word meaning with feature as unit of processing;• Model remains the same.
Speak very brieflySpeak very briefly
02/ 09/ 2000 Thesis proposal --- Raluca Budiu16
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.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu17
Overview
Thesis Topic;Model; Empirical constraints; Computational constraints; Summary and work plan.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu18
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).
02/ 09/ 2000 Thesis proposal --- Raluca Budiu19
Metaphor Position Effects
Metaphor-first sentences take longer to comprehend than metaphor-second sentences(Gerrig & Healy, 1983).
Container contextContainer context
Stars context
Stars context Stars context
Drops of molten silver filled the sky
The sky was filled with drops of molten silver
4.21s4.21s(4.23s)(4.23s)
3.53s3.53s(2.84s)(2.84s)
*
* Predictions
*
02/ 09/ 2000 Thesis proposal --- Raluca Budiu22
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?
02/ 09/ 2000 Thesis proposal --- Raluca Budiu23
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?
02/ 09/ 2000 Thesis proposal --- Raluca Budiu24
Good vs. Bad Illusions
Illusion rates (Ayers et al., 1996) and model predictions
010
2030
4050
60
Undistorted Gooddistortions
Bad distortions
Illu
sio
n R
ate
* 1
00
Data Predictions
All levels of distortion are significantly different from one another.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu27
Modeling Semantic Illusions
take
arkanimals
Noah
Ark prop
agent verb
place-obliquepatient
Mos
es
Adam
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.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu28
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).
02/ 09/ 2000 Thesis proposal --- Raluca Budiu29
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).
02/ 09/ 2000 Thesis proposal --- Raluca Budiu30
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 Dentist’s --- about Bill• At the Doctor’s --- about Tom
Rates of recall per script version (Bower et al., 1979) and model predictions
0
0.1
0.2
0.3
0.4
1 2
Script versions
Ra
te o
f re
ca
ll
Stated actions Data
Stated actionsPredictions
Unstated actionsData
Unstated actionsPredictions
02/ 09/ 2000 Thesis proposal --- Raluca Budiu31
Modeling Script Effects
Story 2 (doctor’s)
Story 1 (dentist’s)
Visiting-healthcare-professional script
Studied Propositions Script Propositions
02/ 09/ 2000 Thesis proposal --- Raluca Budiu33
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 IntrudedBill paid the bill. Tom paid the bill. The nurse x-rayed Bill’s The nurse checked Tom’s
teeth. blood pressure.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu34
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).
02/ 09/ 2000 Thesis proposal --- Raluca Budiu35
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);
02/ 09/ 2000 Thesis proposal --- Raluca Budiu37
Gaze Durations on Homophones
Duffy et al. (1988) manipulated position of disambiguating region and relative frequency of the homophone’s meanings:
– Disambiguating region before/after the homophone;
– Homophone could be balanced (pitcher) or unbalanced (port);
02/ 09/ 2000 Thesis proposal --- Raluca Budiu38
Gaze Duration on Homophones
Mean gaze duration on homophones in Duffy et al. (1988)
240245250255260265270275280285
Before After Before After
Balanced Unbalanced
msec
Ambiguous Control
• Times longer than controls reflect multiple access.• Times equal with controls reflect selective access.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu39
Time Spent on Disambiguating Region
Mean time spent on disambiguating region in Duffy et al. (1988)
01020304050607080
Before After Before After
Balanced Unbalanced
mse
c/ch
ar
Ambiguous Control
mihaib:
hide
mihaib:
hide
02/ 09/ 2000 Thesis proposal --- Raluca Budiu40
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.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu41
Overview
Thesis Topic;Model;Empirical constraints: Computational constraints; Summary and work plan.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu42
Computational Constraints
Realistic reaction times; Integration with background knowledge; Allowing for errors of the syntactic processor (i.e. wrong
thematic roles).
ModelSemantic
Background
Words+
Thematicroles
knowledge
interpretation
02/ 09/ 2000 Thesis proposal --- Raluca Budiu43
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.
ModelSemantic
Background
Words+
Candidate thematicroles
knowledge
interpretation
02/ 09/ 2000 Thesis proposal --- Raluca Budiu44
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.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu45
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).
02/ 09/ 2000 Thesis proposal --- Raluca Budiu46
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);
02/ 09/ 2000 Thesis proposal --- Raluca Budiu47
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).
02/ 09/ 2000 Thesis proposal --- Raluca Budiu48
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”.
02/ 09/ 2000 Thesis proposal --- Raluca Budiu49
Work Plan
Garden path
Lexical ambiguity
Text memory
Semantic illusions
Metaphor
20% 10%15%30%
• Modeling and parameter fitting;• Data collection: metaphors and semantic illusions;• The model still has to solve the more difficult
problems of discourse representation.
25%