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A Brief and Friendly Introduction to Computational Psycholinguistics Roger Levy UC San Diego Department of Linguistics COGS 1 guest lecture February 2, 2010
A Brief and Friendly Introduction toComputational Psycholinguistics
Roger Levy
UC San DiegoDepartment of Linguistics
COGS 1 guest lectureFebruary 2, 2010
What is “computational psycholinguistics”?
◮ Inherently, linguistic communication involves the resolutionof uncertainty over a potentially unbounded set of possiblesignals and meanings.
◮ How can a fixed set of knowledge and resources bedeployed to manage this uncertainty?
This is the study of language processing.
◮ And how cansuch knowledge and resources be learned from finite input?
This is the study of language acquisition.
Computational psycholinguistics studies these problems byconstructing explicit mathematical models and testing themwith experiments.
What is “computational psycholinguistics”?
◮ Inherently, linguistic communication involves the resolutionof uncertainty over a potentially unbounded set of possiblesignals and meanings.
◮ How can a fixed set of knowledge and resources bedeployed to manage this uncertainty?
This is the study of language processing.
◮ And how cansuch knowledge and resources be learned from finite input?
This is the study of language acquisition.
Computational psycholinguistics studies these problems byconstructing explicit mathematical models and testing themwith experiments.
What is “computational psycholinguistics”?
◮ Inherently, linguistic communication involves the resolutionof uncertainty over a potentially unbounded set of possiblesignals and meanings.
◮ How can a fixed set of knowledge and resources bedeployed to manage this uncertainty?
This is the study of language processing.
◮ And how cansuch knowledge and resources be learned from finite input?
This is the study of language acquisition.
Computational psycholinguistics studies these problems byconstructing explicit mathematical models and testing themwith experiments.
What is “computational psycholinguistics”?
◮ Inherently, linguistic communication involves the resolutionof uncertainty over a potentially unbounded set of possiblesignals and meanings.
◮ How can a fixed set of knowledge and resources bedeployed to manage this uncertainty?
This is the study of language processing.
◮ And how cansuch knowledge and resources be learned from finite input?
This is the study of language acquisition.
Computational psycholinguistics studies these problems byconstructing explicit mathematical models and testing themwith experiments.
What is “computational psycholinguistics”?
◮ Inherently, linguistic communication involves the resolutionof uncertainty over a potentially unbounded set of possiblesignals and meanings.
◮ How can a fixed set of knowledge and resources bedeployed to manage this uncertainty?
This is the study of language processing.
◮ And how cansuch knowledge and resources be learned from finite input?
This is the study of language acquisition.
Computational psycholinguistics studies these problems byconstructing explicit mathematical models and testing themwith experiments.
What is “computational psycholinguistics”?
◮ Inherently, linguistic communication involves the resolutionof uncertainty over a potentially unbounded set of possiblesignals and meanings.
◮ How can a fixed set of knowledge and resources bedeployed to manage this uncertainty?
This is the study of language processing.
◮ And how cansuch knowledge and resources be learned from finite input?
This is the study of language acquisition.
Computational psycholinguistics studies these problems byconstructing explicit mathematical models and testing themwith experiments.
What is “language processing”?
◮ Language processing is the study of how humanscomprehend and produce language (sentences, wordswithin sentences, and sequences of sentences, etc.) inreal time.
◮ We can divide this into language comprehension(understanding what is spoken and what is written) andlanguage production (choosing what to say or write basedon what you want to “mean”)
What is “language processing”?
◮ Language processing is the study of how humanscomprehend and produce language (sentences, wordswithin sentences, and sequences of sentences, etc.) inreal time.
◮ We can divide this into language comprehension(understanding what is spoken and what is written) andlanguage production (choosing what to say or write basedon what you want to “mean”)
What is “language acquisition”?
◮ Language acquisition is the study of how humans acquireknowledge of their native language (as infants and aschildren)
Today
In this talk I’ll focus on language comprehension, and thendiscuss a bit about language production.
Theoretical Desiderata
Realistic models of human sentence comprehension mustaccount for:
◮ Language has structure◮ Robustness to arbitrary input◮ Accurate disambiguation◮ Inference on basis of incomplete input (Tanenhaus et al.,
1995; Altmann and Kamide, 1999; Kaiser and Trueswell,2004)
◮ Processing difficulty is differential and localized
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Language has structure
The colored word sequences all have something in common:
◮ The girl gave the dog a big sloppy kiss.◮ I gave the dog a big sloppy kiss.◮ Every boy on the left side of the room gave the dog a big
sloppy kiss.◮ The teacher of this class gave the dog a big sloppy kiss.
In linguistics, this commonality is that the colored wordsequences are all of the same phrase type.In this case, the phrase type is called a noun phrase.Languages have many different phrase types, and we candescribe the grammar of a languages in how its phrase typescome together.
Robustness
Real linguistic input is not always totally well-formed. . .
I think when she finally came to the realization that,you know, no, I can not, I can not take care of myself.. . .I mean, for somebody who is, you know, for most oftheir life has, has, uh, not just merely had a farm buthad ten children had a farm, ran everything becauseher husband was away in the coal mines.And, you know, facing that situation, it’s, it’s quite adilemma.
. . . but usually we come to understand it pretty well anyway.
Robustness
Real linguistic input is not always totally well-formed. . .
I think when she finally came to the realization that,you know, no, I can not, I can not take care of myself.. . .I mean, for somebody who is, you know, for most oftheir life has, has, uh, not just merely had a farm buthad ten children had a farm, ran everything becauseher husband was away in the coal mines.And, you know, facing that situation, it’s, it’s quite adilemma.(The woman is facing being put in a resting home.)
. . . but usually we come to understand it pretty well anyway.
Robustness
Real linguistic input is not always totally well-formed. . .
I think when she finally came to the realization that,you know, no, I can not, I can not take care of myself.. . .I mean, for somebody who is, you know, for most oftheir life has, has, uh, not just merely had a farm buthad ten children had a farm, ran everything becauseher husband was away in the coal mines.And, you know, facing that situation, it’s, it’s quite adilemma.(The woman is facing being put in a resting home.)
. . . but usually we come to understand it pretty well anyway.
Accurate disambiguation
Most sentences are ambiguous in ways we do not even notice:
Mary forgot the pitcher. . .
Accurate disambiguation
Most sentences are ambiguous in ways we do not even notice:
Mary forgot the pitcher. . .
Accurate disambiguation
Most sentences are ambiguous in ways we do not even notice:
Mary forgot the pitcher of water sitting near the stove.
Accurate disambiguation
Most sentences are ambiguous in ways we do not even notice:
Mary forgot the pitcher of water sitting near the stove.
Accurate disambiguation
Most sentences are ambiguous in ways we do not even notice:
Mary forgot the pitcher of water sitting near the stove.
That’s probably not what you were thinking of...
Inference on the basis of incomplete input
Comprehenders do not wait until the whole sentence has beenheard to make inferences about what it means or will wind upmeaning:
(Altmann and Kamide, 1999)
Inference on the basis of incomplete input
Comprehenders do not wait until the whole sentence has beenheard to make inferences about what it means or will wind upmeaning:
(Altmann and Kamide, 1999)
Inference on the basis of incomplete input
Comprehenders do not wait until the whole sentence has beenheard to make inferences about what it means or will wind upmeaning:
“The boy will eat/move the cake. . . ”
(Altmann and Kamide, 1999)
Inference on the basis of incomplete input
Comprehenders do not wait until the whole sentence has beenheard to make inferences about what it means or will wind upmeaning:
“The boy will eat/move the cake. . . ”
(Altmann and Kamide, 1999)
Inference on the basis of incomplete input
Comprehenders do not wait until the whole sentence has beenheard to make inferences about what it means or will wind upmeaning:
“The boy will eat/move the cake. . . ”
That is, comprehension is incremental
(Altmann and Kamide, 1999)
Processing difficulty is differential
Using multiple relative clauses in a sentence can makeprocessing difficult:
This is the malt that the rat that the cat that the dogworried killed ate.
It’s not the meaning of the sentence, or the use of relativeclauses, that makes it hard:
This is the malt that was eaten by the rat that waskilled by the cat that was worried by the dog.
Processing difficulty is differential
Using multiple relative clauses in a sentence can makeprocessing difficult:
This is the malt that the rat that the cat that the dogworried killed ate.
It’s not the meaning of the sentence, or the use of relativeclauses, that makes it hard:
This is the malt that was eaten by the rat that waskilled by the cat that was worried by the dog.
Processing difficulty is differential
Using multiple relative clauses in a sentence can makeprocessing difficult:
This is the malt that the rat that the cat that the dogworried killed ate.
It’s not the meaning of the sentence, or the use of relativeclauses, that makes it hard:
This is the malt that was eaten by the rat that waskilled by the cat that was worried by the dog.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is differential
Did you believe that this sentence was English?
This is the malt that the rat that the cat that the dogworried killed ate.
◮ Consider this simple example:
This is the cat that the dog worried.
◮ And this one:This is the rat that the cat killed.
◮ Which cat did the killing? Suppose it was the cat that thedog worried.
This is the rat that the cat that the dog worriedkilled.
Processing difficulty is localized
[self-paced reading demo, Example1]
(Grodner and Gibson, 2005)
Processing difficulty is localized
[self-paced reading demo, Example1]
(Grodner and Gibson, 2005)
Try to guess the next word in the sentence
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Try to guess the next word in the sentence
My brother came inside to. . .
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Try to guess the next word in the sentence
My brother came inside to. . . chat? get warm? talk? eat? rest?
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Try to guess the next word in the sentence
My brother came inside to. . . chat? get warm? talk? eat? rest?The children went outside to. . .
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Try to guess the next word in the sentence
My brother came inside to. . . chat? get warm? talk? eat? rest?The children went outside to. . . play
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Try to guess the next word in the sentence
My brother came inside to. . . chat? get warm? talk? eat? rest?The children went outside to. . . play
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Try to guess the next word in the sentence
My brother came inside to. . . chat? get warm? talk? eat? rest?The children went outside to. . . play
◮ Empirically, it’s been shown that more highly predictablewords are read more quickly (Ehrlich and Rayner, 1981)
◮ Why would this be the case?
Describing the hierarchical structure of sentences
◮ Sentences are not just sequences of words◮ Some words are closely associated with other words into
PHRASES
◮ These phrases are in turn associated with other words orphrases to form larger phrases
◮ The sentence is the largest phrase◮ We use FORMAL GRAMMARS to describe these phrasal
arrangements◮ The formal grammatical description of a sentence gives us
considerable inroads into understanding its meaning
Describing the hierarchical structure of sentences
◮ Sentences are not just sequences of words◮ Some words are closely associated with other words into
PHRASES
◮ These phrases are in turn associated with other words orphrases to form larger phrases
◮ The sentence is the largest phrase◮ We use FORMAL GRAMMARS to describe these phrasal
arrangements◮ The formal grammatical description of a sentence gives us
considerable inroads into understanding its meaning
Describing the hierarchical structure of sentences
◮ Sentences are not just sequences of words◮ Some words are closely associated with other words into
PHRASES
◮ These phrases are in turn associated with other words orphrases to form larger phrases
◮ The sentence is the largest phrase◮ We use FORMAL GRAMMARS to describe these phrasal
arrangements◮ The formal grammatical description of a sentence gives us
considerable inroads into understanding its meaning
Describing the hierarchical structure of sentences
◮ Sentences are not just sequences of words◮ Some words are closely associated with other words into
PHRASES
◮ These phrases are in turn associated with other words orphrases to form larger phrases
◮ The sentence is the largest phrase◮ We use FORMAL GRAMMARS to describe these phrasal
arrangements◮ The formal grammatical description of a sentence gives us
considerable inroads into understanding its meaning
Describing the hierarchical structure of sentences
◮ Sentences are not just sequences of words◮ Some words are closely associated with other words into
PHRASES
◮ These phrases are in turn associated with other words orphrases to form larger phrases
◮ The sentence is the largest phrase◮ We use FORMAL GRAMMARS to describe these phrasal
arrangements◮ The formal grammatical description of a sentence gives us
considerable inroads into understanding its meaning
Describing the hierarchical structure of sentences
◮ Sentences are not just sequences of words◮ Some words are closely associated with other words into
PHRASES
◮ These phrases are in turn associated with other words orphrases to form larger phrases
◮ The sentence is the largest phrase◮ We use FORMAL GRAMMARS to describe these phrasal
arrangements◮ The formal grammatical description of a sentence gives us
considerable inroads into understanding its meaning
Context-free Grammars
A context-free grammar (CFG) consists of a tuple (N, V , S, R)such that:
◮ N is a finite set of non-terminal symbols;◮ V is a finite set of terminal symbols;◮ S is the start symbol;◮ R is a finite set of rules of the form X → α where X ∈ N
and α is a sequence of symbols drawn from N ∪ V .
A CFG derivation is the recursive expansion of non-terminalsymbols in a string by rules in R, starting with S, and aderivation tree T is the history of those rule applications.
Context-free Grammars: an example
Let our grammar (the rule-set R) be
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
The nonterminal set N is {S, NP, VP, Det , N, P, V}, theterminal set V is {the, dog, cat , near , growled}, and our startsymbol S is S.
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Context-free Grammars: an example II
S →NP VPNP→Det NNP→NP PPPP→P NPVP→V
Det→ theN → dogN → catP → nearV → growled
Here is a derivation and the resulting derivation tree:
S
NP
NP
Det
the
N
dog
PP
P
near
NP
Det
the
N
cat
VP
V
growled
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
Grammar and structural ambiguity◮ Most sentences are ambiguous in ways we don’t even
noticeThe a are of I (Abney, 1996)
◮ are can be a noun: “there are a hundred ares in a hectare”◮ a can be a descriptor (“the a students”)◮ I can be a descriptor that stands in as a full proper noun
◮ Some sentences are am-biguous in ways that we don’t notice without some reflection
I ate the cake with a spoon.
◮ Other sentences are ambiguous in ways that are prettyobvious
The son of the colonel who shot himself was dearlyloved.
◮ One goal of computational psycholinguistics is to give aprecise statement of how the alternative interpretations areconstructed and chosen between
References I
Abney, S. (1996). Statistical methods and linguistics. In Klavans, J.and Resnik, P., editors, The Balancing Act: Combining Symbolicand Statistical Approaches to Language. Cambridge, MA: MITPress.
Altmann, G. T. and Kamide, Y. (1999). Incremental interpretation atverbs: restricting the domain of subsequent reference. Cognition,73(3):247–264.
Ehrlich, S. F. and Rayner, K. (1981). Contextual effects on wordperception and eye movements during reading. Journal of VerbalLearning and Verbal Behavior, 20:641–655.
Grodner, D. and Gibson, E. (2005). Some consequences of the serialnature of linguistic input. Cognitive Science, 29(2):261–290.
Kaiser, E. and Trueswell, J. C. (2004). The role of discourse contextin the processing of a flexible word-order language. Cognition,94:113–147.
Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K., and Sedivy,J. C. (1995). Integration of visual and linguistic information inspoken language comprehension. Science, 268:1632–1634.
