The role of background knowledge in sentence and discourse processing
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The Role of Background Knowledge in Sentence and Discourse Processing. Thesis Proposal Raluca Budiu February 9, 2000. Metaphors. Time is money. People from all cultures use metaphors on an every-day basis, irrespective of their level of education.

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The role of background knowledge in sentence and discourse processing l.jpg

The Role of Background Knowledge in Sentence and Discourse Processing

Thesis Proposal

Raluca Budiu

February 9, 2000


Metaphors l.jpg
Metaphors Processing

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.

Thesis proposal --- Raluca Budiu


Mistakes l.jpg
“Mistakes” Processing

  • 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.

Thesis proposal --- Raluca Budiu


Memory for text l.jpg
Memory for Text Processing

  • 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.

Thesis proposal --- Raluca Budiu


Motivation l.jpg
Motivation Processing

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.

Thesis proposal --- Raluca Budiu


Thesis topic comprehension l.jpg

At the Processingsemantic 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

Thesis proposal --- Raluca Budiu


Overview l.jpg
Overview Processing

  • Thesis topic;

  • A model for sentence comprehension;

  • Empirical constraints;

  • Computational constraints;

  • Summary and work plan.

Thesis proposal --- Raluca Budiu


Semantic interpretation l.jpg

Words Processing

Semantic

+

Model

thematic

roles

interpretation

Background

knowledge

Noah

take

verb

agent

Ark prop

place-oblique

patient

animals

ark

Semantic Interpretation

Understanding a sentence =finding a matching interpretation/context in the background knowledge.

Thesis proposal --- Raluca Budiu


How does the model work l.jpg

take Processing

animals

Noah

ark

Incremental

From left to right

omitting

How Does the Model Work?

How many

did

on the

Farm context

Farm context

Ark context

Ark context

Ark context

father

raise

Noah

take

verb

verb

agent

agent

Farm prop

Ark prop

place-oblique

place-oblique

patient

patient

animals

animals

ark

farm

Thesis proposal --- Raluca Budiu


Model in the absence of context priming l.jpg

= here the model may omit to check all the previous words Processing

Model in the Absence of Context Priming

Read word

Extract Word Meaning

no

yes

Context?

yes

no

Word matches context?

Find context

no

Context found?

no

yes

yes

Old words match?

Thesis proposal --- Raluca Budiu


Context priming l.jpg
Context Priming Processing

Different processing at the beginning and at the end of the sentence.

ark?

How many animals did Noah take on the

Ark story

1. Boat or ship held to resemble that in which Noah and his family were preserved from the Deluge

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

Thesis proposal --- Raluca Budiu


Model with context priming l.jpg

= here the model may omit to check all the previous words Processing

Model With Context Priming

Read word

Extract Context Role

Context role matches word?

yes

no

Find context

Sentence not

comprehended

Context found?

no

yes

yes

no

no

Old words match?

Thesis proposal --- Raluca Budiu


Distributed meaning assumption l.jpg

Speak very briefly Processing

Distributed Meaning Assumption

Bible char

Navigator

meaning

meaning

word

Noah

“Noah”

meaning

meaning

Patriarch

Married

  • Meaning retrieval = extracting word features;

  • Replace word meaning with feature as unit of processing;

  • Model remains the same.

Thesis proposal --- Raluca Budiu


Summary of the model l.jpg
Summary of the Model Processing

  • Incremental;

  • Trial-and-error strategy;

  • Mixture of bottom-upandtop-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.

Thesis proposal --- Raluca Budiu


Overview15 l.jpg
Overview Processing

  • Thesis Topic;

  • Model;

  • Empirical constraints;

  • Computational constraints;

  • Summary and work plan.

Thesis proposal --- Raluca Budiu


Metaphor related phenomena l.jpg
Metaphor-related Phenomena Processing

  • 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).

Thesis proposal --- Raluca Budiu


Metaphor position effects l.jpg
Metaphor Position Effects Processing

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

Thesis proposal --- Raluca Budiu


What are semantic illusions l.jpg
What Are Semantic Illusions? Processing

  • 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?

Thesis proposal --- Raluca Budiu


Semantic illusion datasets l.jpg
Semantic Illusion Datasets Processing

  • 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?

Thesis proposal --- Raluca Budiu


Good vs bad illusions l.jpg
Good vs. Bad Illusions Processing

All levels of distortion are significantly different from one another.

Thesis proposal --- Raluca Budiu


Modeling semantic illusions l.jpg
Modeling Semantic Illusions Processing

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

Thesis proposal --- Raluca Budiu


Memory for text22 l.jpg
Memory for Text Processing

  • 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).

Thesis proposal --- Raluca Budiu


Text memory datasets l.jpg
Text Memory Datasets Processing

  • 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).

Thesis proposal --- Raluca Budiu


Interferences among related stories l.jpg
Interferences Among Related Stories Processing

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

Thesis proposal --- Raluca Budiu


Modeling script effects l.jpg

Story 1 (dentist’s) Processing

Story 2 (doctor’s)

Modeling Script Effects

Visiting-healthcare-professional

script

Script Propositions

Studied Propositions

Thesis proposal --- Raluca Budiu


Difficulties with modeling script effects l.jpg
Difficulties With Modeling Script Effects Processing

  • 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 Bill’s The nurse checked Tom’s

  • teeth. blood pressure.

Thesis proposal --- Raluca Budiu


Lexical ambiguity resolution l.jpg
Lexical Ambiguity Resolution Processing

  • 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).

Thesis proposal --- Raluca Budiu


Lexical ambiguity datasets l.jpg
Lexical Ambiguity Datasets Processing

  • Gaze duration on balanced and unbalanced homophones (Duffy et al., 1988);

  • Mean reading time per character in the disambiguation region (Duffy et al., 1988);

Thesis proposal --- Raluca Budiu


Gaze durations on homophones l.jpg
Gaze Durations on Homophones Processing

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);

Thesis proposal --- Raluca Budiu


Gaze duration on homophones l.jpg
Gaze Duration on Homophones Processing

  • Times longer than controls reflect multiple access.

  • Times equal with controls reflect selective access.

Thesis proposal --- Raluca Budiu


Time spent on disambiguating region l.jpg

mihaib: Processing

hide

Time Spent on Disambiguating Region

Thesis proposal --- Raluca Budiu


Fitting the data l.jpg
Fitting the Data Processing

  • 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.

Thesis proposal --- Raluca Budiu


Overview33 l.jpg
Overview Processing

  • Thesis Topic;

  • Model;

  • Empirical constraints:

  • Computational constraints;

  • Summary and work plan.

Thesis proposal --- Raluca Budiu


Computational constraints l.jpg

Words Processing

Semantic

+

Model

Thematic

roles

interpretation

Background

knowledge

Computational Constraints

  • Realistic reaction times;

  • Integration with background knowledge;

  • Allowing for errors of the syntactic processor (i.e. wrong thematic roles).

Thesis proposal --- Raluca Budiu


Syntactic ambiguity as a computational constraint l.jpg

Words Processing

Semantic

+

Model

Candidate thematic

roles

interpretation

Background

knowledge

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.

Thesis proposal --- Raluca Budiu


Summary l.jpg
Summary Processing

  • 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.

Thesis proposal --- Raluca Budiu


Empirical constraints l.jpg
Empirical Constraints Processing

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).

Thesis proposal --- Raluca Budiu


Empirical constraints contd l.jpg
Empirical Constraints (contd.) Processing

  • 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);

Thesis proposal --- Raluca Budiu


Empirical constraints contd39 l.jpg
Empirical Constraints (contd.) Processing

  • 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).

Thesis proposal --- Raluca Budiu


Model validation l.jpg
Model Validation Processing

  • 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”.

Thesis proposal --- Raluca Budiu


Work plan l.jpg
Work Plan Processing

  • Modeling and parameter fitting;

  • Datacollection: metaphors and semantic illusions;

  • The model still has to solve the more difficult problems of discourse representation.

Garden

path

Metaphor

Lexical

ambiguity

Semantic

illusions

Text

memory

20%

30%

15%

10%

25%

Thesis proposal --- Raluca Budiu


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