A Brief and Friendly Introduction to Computational Psycholinguistics - - PowerPoint PPT Presentation

A Brief and Friendly Introduction to Computational Psycholinguistics

Roger Levy

UC San Diego Department of Linguistics

COGS 1 guest lecture February 2, 2010

What is “computational psycholinguistics”?

◮ Inherently, linguistic communication involves the resolution

• f uncertainty over a potentially unbounded set of possible

signals and meanings.

◮ How can a fixed set of knowledge and resources be

deployed to manage this uncertainty? This is the study of language processing.

◮ And how can

such knowledge and resources be learned from finite input? This is the study of language acquisition. Computational psycholinguistics studies these problems by constructing explicit mathematical models and testing them with experiments.

What is “computational psycholinguistics”?

◮ Inherently, linguistic communication involves the resolution

• f uncertainty over a potentially unbounded set of possible

signals and meanings.

◮ How can a fixed set of knowledge and resources be

deployed to manage this uncertainty? This is the study of language processing.

◮ And how can

such knowledge and resources be learned from finite input? This is the study of language acquisition. Computational psycholinguistics studies these problems by constructing explicit mathematical models and testing them with experiments.

What is “computational psycholinguistics”?

◮ Inherently, linguistic communication involves the resolution

• f uncertainty over a potentially unbounded set of possible

signals and meanings.

◮ How can a fixed set of knowledge and resources be

deployed to manage this uncertainty? This is the study of language processing.

◮ And how can

such knowledge and resources be learned from finite input? This is the study of language acquisition. Computational psycholinguistics studies these problems by constructing explicit mathematical models and testing them with experiments.

What is “computational psycholinguistics”?

◮ Inherently, linguistic communication involves the resolution

• f uncertainty over a potentially unbounded set of possible

signals and meanings.

◮ How can a fixed set of knowledge and resources be

deployed to manage this uncertainty? This is the study of language processing.

◮ And how can

such knowledge and resources be learned from finite input? This is the study of language acquisition. Computational psycholinguistics studies these problems by constructing explicit mathematical models and testing them with experiments.

What is “computational psycholinguistics”?

◮ Inherently, linguistic communication involves the resolution

• f uncertainty over a potentially unbounded set of possible

signals and meanings.

◮ How can a fixed set of knowledge and resources be

deployed to manage this uncertainty? This is the study of language processing.

◮ And how can

such knowledge and resources be learned from finite input? This is the study of language acquisition. Computational psycholinguistics studies these problems by constructing explicit mathematical models and testing them with experiments.

What is “computational psycholinguistics”?

◮ Inherently, linguistic communication involves the resolution

• f uncertainty over a potentially unbounded set of possible

signals and meanings.

◮ How can a fixed set of knowledge and resources be

deployed to manage this uncertainty? This is the study of language processing.

◮ And how can

such knowledge and resources be learned from finite input? This is the study of language acquisition. Computational psycholinguistics studies these problems by constructing explicit mathematical models and testing them with experiments.

What is “language processing”?

◮ Language processing is the study of how humans

comprehend and produce language (sentences, words within sentences, and sequences of sentences, etc.) in real time.

◮ We can divide this into language comprehension

(understanding what is spoken and what is written) and language production (choosing what to say or write based

• n what you want to “mean”)

What is “language processing”?

◮ Language processing is the study of how humans

comprehend and produce language (sentences, words within sentences, and sequences of sentences, etc.) in real time.

◮ We can divide this into language comprehension

(understanding what is spoken and what is written) and language production (choosing what to say or write based

• n what you want to “mean”)

What is “language acquisition”?

◮ Language acquisition is the study of how humans acquire

knowledge of their native language (as infants and as children)

Today

In this talk I’ll focus on language comprehension, and then discuss a bit about language production.

Theoretical Desiderata

Realistic models of human sentence comprehension must account 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 word sequences 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 can describe the grammar of a languages in how its phrase types come 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 of their life has, has, uh, not just merely had a farm but had ten children had a farm, ran everything because her husband was away in the coal mines. And, you know, facing that situation, it’s, it’s quite a dilemma. . . . 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 of their life has, has, uh, not just merely had a farm but had ten children had a farm, ran everything because her husband was away in the coal mines. And, you know, facing that situation, it’s, it’s quite a dilemma.

(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 of their life has, has, uh, not just merely had a farm but had ten children had a farm, ran everything because her husband was away in the coal mines. And, you know, facing that situation, it’s, it’s quite a dilemma.

(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 been heard to make inferences about what it means or will wind up meaning: (Altmann and Kamide, 1999)

Inference on the basis of incomplete input

Comprehenders do not wait until the whole sentence has been heard to make inferences about what it means or will wind up meaning: (Altmann and Kamide, 1999)

Inference on the basis of incomplete input

Comprehenders do not wait until the whole sentence has been heard to make inferences about what it means or will wind up meaning: “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 been heard to make inferences about what it means or will wind up meaning: “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 been heard to make inferences about what it means or will wind up meaning: “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 make processing difficult: This is the malt that the rat that the cat that the dog worried killed ate. It’s not the meaning of the sentence, or the use of relative clauses, that makes it hard: This is the malt that was eaten by the rat that was killed by the cat that was worried by the dog.

Processing difficulty is differential

Using multiple relative clauses in a sentence can make processing difficult: This is the malt that the rat that the cat that the dog worried killed ate. It’s not the meaning of the sentence, or the use of relative clauses, that makes it hard: This is the malt that was eaten by the rat that was killed by the cat that was worried by the dog.

Processing difficulty is differential

Using multiple relative clauses in a sentence can make processing difficult: This is the malt that the rat that the cat that the dog worried killed ate. It’s not the meaning of the sentence, or the use of relative clauses, that makes it hard: This is the malt that was eaten by the rat that was killed 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 dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

Processing difficulty is differential

Did you believe that this sentence was English? This is the malt that the rat that the cat that the dog worried 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 the

dog worried. This is the rat that the cat that the dog worried killed.

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 predictable

words 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 predictable

words 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 predictable

words 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 predictable

words 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 predictable

words 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 predictable

words 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 predictable

words 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 or

phrases 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 or

phrases 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 or

phrases 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 or

phrases 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 or

phrases 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 or

phrases 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-terminal symbols in a string by rules in R, starting with S, and a derivation tree T is the history of those rule applications.

Context-free Grammars: an example

Let our grammar (the rule-set R) be S →NP VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → growled The nonterminal set N is {S, NP, VP, Det, N, P, V}, the terminal set V is {the, dog, cat, near, growled}, and our start symbol S is S.

Context-free Grammars: an example II

S →NP VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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 VP NP→Det N NP→NP PP PP→P NP VP→V Det→ the N → dog N → cat P → near V → 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

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

Grammar and structural ambiguity

◮ Most sentences are ambiguous in ways we don’t even

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

Grammar and structural ambiguity

◮ Most sentences are ambiguous in ways we don’t even

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

Grammar and structural ambiguity

◮ Most sentences are ambiguous in ways we don’t even

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

Grammar and structural ambiguity

◮ Most sentences are ambiguous in ways we don’t even

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

Grammar and structural ambiguity

◮ Most sentences are ambiguous in ways we don’t even

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

Grammar and structural ambiguity

◮ Most sentences are ambiguous in ways we don’t even

notice

The 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 pretty

• bvious

The son of the colonel who shot himself was dearly loved.

◮ One goal of computational psycholinguistics is to give a

precise statement of how the alternative interpretations are constructed and chosen between

References I

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