Rambow Slides Leysin Tag

8/3/2019 Rambow Slides Leysin Tag

1/142

Tree Adjoining Grammar:

Introduction

Owen RambowCCLS, Columbia University

[email protected]

2008-03-12


2/142

Overview

Terminology (ppt)

What is Tree Adjoining Grammar?

Some Syntactic Analyses

TAG and Syntactic Theory

Problem: German


3/142

Three Main Points About Tree Adjoining

Grammar (TAG):

TAG is a constrained mathematical

formalism

TAG supports the development of lexicalized

grammars for natural languages

TAG is not a linguistic theory


4/142

Constrained mathematical formalisms

Mathematical device for specifying sets of

structures

Constrained: mathematical device cannot

specify all possible sets


5/142

Why constrained mathematical formalisms?

Linguistically appealing because scope oflinguistic theory is restricted

Computationally appealing because of

efficient processing models


6/142

Lexicalization: central role of lexicon in syntax

Many syntactic phenomena are idiosyncraticto specific lexical items or classes of lexical

items

Natural language processing: use of corpora

pervasive, corpora (typically) consist of words


7/142

Reminder: Lexical Idiosyncracies in Syntax

(Subcategorization)

(1) a. He told a secret to Mary

b. He told Mary a secret

c. He divulged a secret to Mary

d. *He divulged Mary a secret


8/142

Lexicalization

A formalism is lexicalized if every elementary

structure contains at least one terminal symbol (=

lexical item)


9/142

Which Constrained Mathematical Formalism?

Context-Free Grammar (CFG) (Chomsky

1957), used all over NLP, BUT


10/142



1957), used all over NLP, BUT Wrong descriptive level (issue of lexicalization)

Not powerful enough formally (Shieber 1985

on Swiss German)


11/142





on Swiss German)

Most current linguistic formalisms go beyond

CFG (TAG, HPSG, LFG)


12/142





on Swiss German)

Most current linguistic formalisms go beyond

CFG (TAG, HPSG, LFG)

Most of these lose constraint on formal power

EXCEPT TAG


13/142

Lets Start with a CFG

(2) a. S NP VP

b. VP really VP

c. VP V NP

d. V likes

e. NP John

f. NP Lyn

Elementary structures of this grammar: the rules


14/142

Derivation in a CFG

Rule 1: S NP VP

Rule 2: VP really VP

Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn


15/142

Derivation in a CFG

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn

S = NP VP (Rule 1, then Rule 5)


16/142

Derivation in a CFG

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn


= John VP (next Rule 2)


17/142

Derivation in a CFG

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn



=

John really VP (next Rule 3)


18/142

Derivation in a CFG

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn



=

John really VP (next Rule 3)= John really V NP (next Rule 6)


19/142

Derivation in a CFG

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn



=

John really VP (next Rule 3)= John really V NP (next Rule 6)

= John really V Lyn (next Rule 4)


20/142

Derivation in a CFG

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn



= John really VP (next Rule 3)

= John really V NP (next Rule 6)

= John really V Lyn (next Rule 4)


21/142

= John really likes Lyn


22/142

CFG is a string-rewriting system

Elementary structure: context-free rewrite rule

Operation: rewrite

Record of derivation is a phrase-structure tree,

called derivation tree


23/142

Derivation Tree:

really

VP

John

S

NP

V

VP

likes Lyn

NP

S=

NP VP (Rule 1)= John VP (Rule 5)

= John really VP (Rule 2)

= John really V NP (Rule 3)

= John really V Lyn (Rule 6)

= John really likes Lyn (Rule 4)


24/142

Lexicalization (reminder)


structure contains at least one terminal symbol (=

lexical item)

Rule 1: S NP VP

Rule 2: VP really VPRule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn

Is this grammar lexicalized?


25/142

Lexicalization (reminder)


structure contains at least one terminal symbol (=lexical item)

Rule 1: S NP VP


Rule 3: VP V NP

Rule 4: V likes

Rule 5: NP John

Rule 6: NP Lyn

Is this grammar lexicalized?

No


26/142

Lexicalizing a CFG (ctd)

Can we lexicallize this CFG?


27/142



Greibach Normal Form: changes rules,

linguistically unappealing


28/142





S NP likes NP


29/142





S NP likes NP

What about really?

Go from string rewriting to tree rewriting!


30/142

Why not make trees the elementary structures of

a grammar?

NP

V

VP

S

likes

NP

NP

John Lyn

NP

1

2 3

Need operations to combine these trees!


31/142

Substitution

A

=>

S SA

A


32/142

Substitution

32

41

NP

V

VP

S

likes

NP

Lyn

NPNP

John

VP

NP

Lynlikes

VJohn

S

NP


33/142

New formalism: Tree Substitution Grammar

(TSG)

Elementary structures: phrase-structure trees

Operations: substitution

Comparison with CFG


34/142

Comparison with CFG

Formal power?

Comparison with CFG


35/142

Comparison with CFG

Formal power?

Equivalent to CFG

Linguistic expressive power (what range of

theories can we formulate)?


36/142

Comparison with CFG

Formal power?

Equivalent to CFG



Greater than CFG; examples: agreement,

subcategorization; extended domain oflocality


37/142

Agreement

[number=pl]NP

NP

like [number=pl]

S

VP

V

1

[number=sg, person=3]NP

NP

likes [number=sg,person=3]

S

VP

V

1


38/142

Subcategorization

V

VPNP0

S

tell

V

VP

tell

NP1NP2

NP0

to

NP1 PP

P NP2

S

Comparison with CFG (ctd)


39/142


Linguistic adequacy issue:

G ( )


40/142


Linguistic adequacy issue:

But what about really?

Adj ti


41/142

Adjunction

=>A

S SA

A*

A

A

Adjunction


42/142

Adjunction

1 54

John

S

NPNP

V

VP

S

John

likes Lyn

NP

V

likes Lyn

NP

really VP*

VP

VP

VPreally

N f m lism Tree Adjoining Grammar (TAG)


43/142

New formalism: Tree Adjoining Grammar (TAG)

Elementary structures: phrase-structure trees

Operations: substitution, adjunction

Note: trees that can be adjoined are called

auxiliary trees, trees that can be substituted

initial trees

Comparison with CFG and TSG


44/142

p

Formal power?



45/142

Formal power?

Greater than CFG and TSG





46/142


Formal power?

Greater than CFG and TSG



Greater than CFG, but same as TSG;

examples: agreement, subcategorization;extended domain of locality

Derivation of our sentence


47/142

Derivation of our sentence

NP

V

VP

S

likes

NP

really VP

VP

John

NP

Lyn

NP

really VP

VP

NP

Lynlikes

V

NP

S

John

Derivation structure?


48/142

Derivation structure?

NP

V

VP

S

likes

NP

really VP

VP

John

NP

Lyn

NP

really VP

VP

NP

Lynlikes

V

NP

S

John

like

Lyn reallyJohn

2

John

ADJUNCT

OBJ

SUBJ

2.2

1

like

Lyn really

A note on elementary structures and rewriting


49/142

CFG: elementary structures are rewrite rulesS NP VP

TAG: elementary structures are PS trees



50/142


CFG: elementary structures are rewrite rulesS NP VP

TAG: elementary structures are PS treesTAG trees are really tree rewrite rules:

VP

VP

NPV

really

NP

S

likes

really VP

VP

VPS

likes

NP

S

VP

V

NP

Summary of comparison CFG, TSG, TAG


51/142

Summary of comparison CFG, TSG, TAG

CFG TSG TAG

Elementary Struc. string PS tree PS tree

Derived Structure string PS tree PS tree

Derivation Struc. PS tree Dep tree Dep tree

Can lexicalize Engl? no no yes

Overview


52/142

Terminology (ppt)




Problem: German

Why TAGs are useful in Linguistics


53/142

y g

Constrained mathematical formalism

Extended domain of locality

Extended Domain of Locality


54/142

Elementary structure is a tree, not a single layerof a tree

CFG: VP V NP[obj]TAG:

S


55/142

S

rr

rrr

NP

[case:nom]

[agr: 1 ]

VP

rrr

V

[agr: 1 ]

/eat/

NP

[case:obj]

We can use Extended Domain of Locality for:


56/142

Case requirements imposed on complements(example: German objects)

Agreement through co-referential feature

structures between different parts of the tree

(e.g., subject-verb agreement or object

clitic-participle agreement in F) Strongly governed prepositions

Subcatgorization frame of lexical item (basicsubcat and variations)

Syntax of lexical item (basic and variation)

Idioms


57/142

Note: lexical item = single word or word+prepor idiom

Strongly governed preposition


58/142

S


59/142

S

rr

rr

rrr

NP

[nom,agr: 1 ]

VP

rr

rr

r

V[agr: 1 ]

/discriminate/

PP

rrr

P

against

NP

[obj]

Lois discrminates against linguists

Subcategorization frame:


60/142

S

rr

r

NP VP

rr

V

/eat/

NP

S


61/142

rr

rrr

NP VP

rr

rrr

V

/give/

NP PP

rr

P

to

NP

Substitution nodes (or foot nodes) designate

required arguments

Note: adjuncts handled through adjunction


62/142

S

rr

r

NP VP

rr

V

/eat/

NP

VP


63/142

rr

AdvP

often

VP*

Mary often eats beans

Adjuncts are not required, but the adjunct can

only modify certain trees

English Passive:


64/142

S

rr

rr

rr

NP[nom,role:theme

agr: 1 ]

VP

rr

r

Aux

[agr: 1 ]

/be/

VP

V

[past-part]

eaten

Apples were eaten

Fancy English syntax (topicalization):


65/142

S

rr

rrr

NP[obj]

S

rr

rr

NP

[nom,agr: 1 ]

VP

V

[agr: 1 ]

/eat/

Apples John eats

Note: trace optional


66/142

Idiom


67/142

S

rr

rr

rr

NP

[nom,agr: 1 ]

VP

rr

rr

V[agr: 1 ]

/kick/

NP

rr

the N

bucket

Bernice kicks the bucket (= Bernice dies)

Examples of Derivations


68/142

(1) Subcategroization for clauses

(2) Long-distance topicalization

(3) Control verbs

(4) Raising

(5) Extraction from picture-NPs

Subcategorization for clauses


69/142

Approach 1: Substitution

S

rr

rrr

NP

[nom,agr: 1 ]

VP

r

rr

V

[agr: 1 ]

/think/

S

S


70/142

rr

rrr

r

COMP

that

S

rr

rrr

NP

[nom,agr: 1 ]

VP

rrr

V

[agr: 1 ]

/like/

NP

S

r


71/142

rrr

rr

NP

[nom,agr: 1 ]

VP

rr

rr

V

[agr: 1 ]

/think/

S

rr

rr

COMP

that

S

rrrr

NP

[nom,agr: 2 ]

VP

rr

V

[agr: 2 ]

/like/

NP

Ivan thinks that Natasha likes pears



72/142

Approach 2: AdjunctionS

S

rr

rr

VP

rrr

V

/think/

S*

S

r

rrr

COMP

that

S

rr

r

NP VP

rr

V

/like/

NP

S

rr


73/142

rrr

rr

NP

[nom,agr: 1 ]

VP

rr

rr

V

[agr: 1 ]

/think/

S

rr

rr

COMP

that

S

rrrr

NP

[nom,agr: 2 ]

VP

rr

V

[agr: 2 ]

/like/

NP

Ivan thinks that Natasha likes pears



74/142

Approach 2: Adjunction (ctd)

Exactly the same derived tree (except for S root

node of matrix clause)

Advantage of adjunction: extraction from

embedded clause (coming up!)

Long-distance topicalization and wh-movement


75/142

S

rr

rr

NP VP

rrr

V

/think/

S*

S

rr

r

NP S

rr

NP VP

V

/eat/

S

rrr


76/142

r

NP S

rrr

NP VP

rrr

V

/think/

S

rr

NP VP

V

/eat/

Cherries Belinda thinks Jo eats

S

S

S

rrr


77/142

S

rrr

NP VP

rr

V

/think/

S*

r

NP S

rrr

COMP

that

S

rr

NP VP

V

/eat/

S

rr

r


78/142

r

NP S

S

rr

r

NP VP

rr

r

V

/think/

S

rrr

COMP

that

S

rr

NP VP

V

/eat/

Cherries Belinda thinks that Jo eats

Long-distance wh-movement (ctd)


79/142

S

rr

r


80/142

NP S

S

rr

rr

NP VP

rr

rr

V

/think/

S

rr

rr

COMP

that

S

rr

rr

NP VP

rr

r

V

/suspect/

S

rrr

COMP

that

S

rr

NP VP

Cherries Belinda thinks that Mark suspects that

Jo eats


81/142

Control verbs


82/142

S

rr

rrr

NP

[ref: 1 ]

VP

rr

rr

V

wishes

S*

[mood:inf-to]

[control: 1 ]

S


83/142

[mood:inf-to]

[control: 1 ]

rr

rr

NP

PRO

[index: 1 ]

VP

rrr

V

to sing

NP

Emily wishes to sing songs

Control verbs


84/142

S

rr

rr

rr

NP VP

rr

rr

rr

r

V

asks

NP

[ref: 1 ]

S*

[mood:inf-to]

[control: 1 ]

S


85/142

[mood:inf-to]

[control: 1 ]

rr

rr

NP

PRO

[index: 1 ]

VP

rrr

V

to sing

NP

Emily asks Alfred to sing songs

Raising verbs

VP S


86/142

[agr: 1 ]

rrr

V

[agr: 1 ]

/seem/

VP*

rr

r

NP

[agr: 1 ]

[agr: 1 ]

VP

[agr:-]

V

to sing

S

r

rr


87/142

rr

NP

[agr: 1 ]

VP

[agr: 1 ]

rrr

V

[agr: 1 ]

/seem/

VP

[agr:-]

V

to sing

Emily seems to sing

Raising verbs (ctd)

S

r


88/142

rr

rr

NP

[agr: 1 ]

VP

[agr: 1 ]

rrr

V

[agr: 1 ]

/seem/

VP

rr

V

to appear

VP

V

to sing

Emily seems to appear to sing

Picture-NP extraction

S


89/142

S

rr

rr

NP VP

rrr

V

/paint/

NP

NP

rrr


90/142

r

NP

pictures

PP

rr

P

of

NP

John paints pictures of bridges

How do we get Bridgesi John paints pictures of ti?

Picture-NP extraction (ctd)

S

rr


91/142

rrr

NP VP

rr

V

/paint/

NP

{ S

rr

NP

[index= 1 ]

S*

, NP

rrr

NP

pictures

PP

rr

P

of

NP

[index= 1 ]

}

S

rr

rr

NP S


92/142

NP

[index= 1 ]

S

rr

r

NP VP

rr

r

V

/paint/

NP

rrr

NP

pictures

PP

rr

P

of

NP

[index= 1 ]

Bridgesi John paints pictures of ti

Overview

Terminology (ppt)


93/142

Terminology (ppt)




Problem: German

The Lexicon as Grammar

Q: Where do all these trees come from? Arent

generalizations being missed (wh movement the


94/142

generalizations being missed (wh-movement the

same for like and dislike)?

A: We can generalize across classes of lexicalitems (eg, Transitive-Verb), and associate lexical

item with class (eg, like is a Transitive-Verb)

The Lexicon as Grammar

Q: Where do all these trees come from? Arent


95/142

Q: Where do all these trees come from? Aren tgeneralizations being missed (wh-movement the

same for like and dislike)?

A: We can generalize across classes of lexical

items (eg, Transitive-Verb), and associate lexical

item with class (eg, like is a Transitive-Verb)

Q: Still, wh-movement from the subject position is

the same in intransitive, transitive, and ditransitive

verbs isnt a generalization being missed?

A: Generating all possible trees is the goal of a

TAG-based theory of syntax

The Lexicon as Grammar (ctd)

Q: How big are the elementary trees?


96/142

Q: How big are the elementary trees?A: A tree is a lexical item and its syntactic

projection.

Option 1: DP

rr

Det

the

NP

NP

tree

Option 2: DP

rr

D NP

Det


97/142

Det NP

tree

the

Option 3: NP

tree

NP

r

r

the NP*

The exact definition is subject to the particular

linguistic theory.

TAG is not a linguistic theory (nor a linguistic

framework)

Lik CFG TAG i th ti l f li


98/142

Like CFG, TAG is a mathematical formalism

Unlike GPSG, HPSG, LFG, CCG, and others, TAG

is not a combination theory-and-formal-framework

This means: a linguistic theory (or a linguistic

framework) must be added to TAG to use it for

linguistic description and/or theorizing

We speak of TAG-based linguistic theories/

approaches/frameworks

What is a TAG-based linguistic theory?

Model: GB


99/142

Model: GBLexicon

andParameters

D-Structure S-Structuremove-

Principles

constrains

constrain constrain constrain

Model: Minimalism

project

(Spellout)


100/142

move-

Multiclausal

LFmove-

move-

andParameters

Principles

Lexicon

project

extend-target

insert

Multiclausal

PF

constrainconstrain constrain

Model: TAG-Based Syntax

Lexicon D-Structure S-Structureconstrains

Clausal Clausal

move- formal

derivation

S-Structure

Multiclausal


101/142

andParameters

Principles


Model: GBLexicon

andParameters

D-Structure S-Structuremove-

Principles

constrains


TAG-Based Syntactic Theory

Syntactic theory explains variation on domainsf


102/142

Syntactic theory explains variation on domainsof extended projections only

Combination of extended projections only

through the formal operations of substitution

and adjunction

Scope of theory greatly reduced!

See Frank (2000) for sample account

Overview

Terminology (ppt)


103/142

gy (pp )




Problem: German

German: long scrambling in Mittelfeld

(3) a. dass es Hans zu reparieren versucht


104/142

( )that it Hans to

prepair tries

that Hans tries to repair it

b. * dassthat

esit

HansHans

repariertrepair

zuto

habenhave

bereutregrets

Intended meaning: that Hans regrets

having repaired it

Long scrambling is iterable and combinable with

extraposition


105/142

(4) a. dassthat

esitACC

HansHans

den Kindern[the children]DAT

zuto

gebengive

versuchttries

that Hans tries to give it to the children

b. dass es Hans den Kindern versucht zu

geben

dass es Hans den Kindern versucht zu geben

=>A

S SA

A


106/142

=>A

A*

A

A

TAG adjunction produces only 5 segments, we

need 6!

Solution: V-TAG, UVG-DL, DSG (Rambow 1994,

Rambow et al 2001)

TAG: grammar = ready-made trees; trees are


107/142

g y ;

combined

Solution: V-TAG, UVG-DL, DSG (Rambow 1994,

Rambow et al 2001)

TAG: grammar ready made trees; trees are


108/142

TAG: grammar = ready-made trees; trees are

combined

DSG: grammar = trees in parts, assembly

required; assembled trees are combined

Example:arg: +

NP

VP arg: +

VPNP

VP

VP

arg: +VP

VP

VP

VP gebenNP

head: +


109/142

path constraint:no [ complete:+ ]

NP VPNP VPVPnom

VP gebenNP

VP

V

arg: dat acc

path constraint:

no [ head:+ ]

head:+ arg:+

head:+ arg:+VPNP

VP

acc


110/142

arg: +

path constraint:

no [ complete:+ ]

NP

VP arg: +

VPNP

VP

VP

arg: +VP

VPnom

VP

VP gebenNP

VP

V

head: +

arg: dat acc

path constraint:

no [ head:+ ]

head:+ arg:+

VP

V

geben

VP

VP

nomNP

NPdat

acc

head: arg:

head:+ arg:+head:+ arg:

Example with Long Scrambling:

NP

VP

VP

arg: +

NP

VP

VP

arg: +arg: +VP

VPNP


111/142

arg: arg:

accdatnom

path constraint:

no [ complete:+ ]

arg: +

head: + VP

VP geben

V

versuchenVP

VP head: +

V

VP

Example with Long Scrambling:

VP arg: +d t

NP

VP arg: +


112/142

arg:

arg:

arg: +VP

nomNP

acc

NP

dat

versuchenVP

VP head: +

V

VP arg: +

head: + VP

VP geben

V

New Model: V-TAG-Based SyntaxClausal

formalderivation

Lexicon

constrain

Structureformalderivation

S-Structure

Multiclausal


113/142

and Parameters

Principles

Old Model

Lexicon

andParameters

D-Structure S-Structure

Principles

constrains

Clausal Clausal

move- formalderivation

S-Structure

Multiclausal


What about LF? Use Synchronous TAG (Shieber

1994, 2000)ClausalStructure

MulticlausalLF


114/142

PF

derivation

synchronizedformalderivation

synchronizedLexicon

and

Parameters

Principles

constrain

Clausal

Structure

Multiclausal

formal

Conclusion

Tree Adjoining Grammar is a good formalismfor describing natural language syntax


115/142

g g g y

Exact type of TAG, and exact type of

linguistic theory, are active areas of research

Backup Slides


116/142

Desiderata for a formal system for NL syntax

(based on mildly context-sensitive, Joshi 1987):

Constrained weak generative capacity (at


117/142

most context-sensitive)

Sentences made up incrementally from lexicalitems: constant-growth property

Can form a new sentence using conjunction:closure under Kleene-star

Sentential subjects: closure under iterated

substitution

Polynomially parsable

Adjunction constraints on nonterminal nodes in

elementary trees:


118/142

Null adjoining constraint (NA) (=terminalnode for rewriting)

Obligatory adjunction constraint (OA)

(=nonterminal node for rewriting)

Selective adjoining constraint (SA)

Increases formal power

Example of adjoining constraint

1 2


119/142

**

S

NP VP

V

seen

OA( , ) 1 2

VP

VP VP

VP

Aux Aux

has is

1 2

TAG with Feature Structures

Represent adjoining constraints with fixed-sized

feature structures


120/142

b3

t1 v t2

t3A

t2

A

A

b1 v b3

t3

b2

t1

b2A

b1A

Example

tense: +

S

VP

1

tense: +

tense: +

tense: -

tense: +

tense:

VP

2

tense: +

tense:

t


121/142

**

*

tense: -

tense: +

NP VP

V

seen

VPAux

has

tense:

tense:

VPAux

is

tense:

tense: -

VP

VP

Aux

been

3

tense: -

tense:

tense:

tense: -

What about questions?

S

tense: [1]

tense: +

tense: [1]

VP

tense: -

tense: +

tense:

VP tense: +

tense:

tense:

1 2


122/142

*

**

*VP

V

seen

tense: -

[ ]

VPAux

has

tense:VPAux

is

tense:

tense: -

Aux

has

tense:

tense: -

tense: +

tense:

Aux

is

tense: +

tense:

tense:

tense: -

4S

S

S

S

3

NP

The Lexicon as Grammar (or the Grammar as

Lexicon)

We have many choices how to define elementary


123/142

trees in a linguistic grammar

S

rrr

NP

Betty

VP[agr:3sg]

VP

[agr:3sg]

V

blushes

S

rr

rr

NP VP


124/142

Betty

rrr

V

[agr:3sg]

NP

bats

V

[agr:3sg]

likes

(These are stupid elementary trees.)


125/142


Almost all linguistic uses of TAG follow certain

basic assumptions we will discuss those basic


126/142

assumptions here

But the basic assumptions are assumptions

linguists bring to TAG, not assumptions that TAG

imposes on the linguists!

For example, we can use EDL for capturing

linguistic properties as just discussed, but TAG

does not impose that choice


It seems that in language:

Properties (morphological, syntactic,

ti ) i t d ith l i l it


127/142

semantic) are associated with lexical items

and with classes of lexical itemsTAG: Given the extended domain of locality,

we can state all relevant properties in an

elementary tree

The properties of larger structures are

compositionally derivable from the propertiesof the composing lexical items

TAG: Grammar is set of trees each

associated with a single lexical item

(exploiting lexicalization)


128/142


A better set of elementary trees:

Srr

Sr


129/142

rr

rr

NP

[agr:3sg]

VP

V

[agr:3sg]

blushes

rr

rr

NP

[agr:3sg]

VP

rr

V

likes

NP

NP

Betty

NP

bats


130/142


Q: In a TAG grammar, is there one tree per lexical

item?

A: No


131/142

A: No

Example: verb

Voice alternations (active/passive)

Syntactic alternations (wh-movement,topicalization, dative shift, . . . )

. . . Every lexical item is associated with a set of

trees (family) which represents the set of

syntactic (and morphological) variations


132/142


Conclusion:

A TAG grammar (in the formal sense) is


133/142

an enumeration of all lexical items in the

language, in all possible syntactic

variations

The formal TAG grammar is a lexicon of thelanguage

English Passive:


134/142

S

rrr

rrr

NP VP


135/142

NP

[nom,role:themeagr: 1 ]

VP

rr

rr

Aux

[agr: 1 ]

/be/

VP

V

[past-part]

eaten

VP

rrr

VP* PP rr


136/142

r

P

by

NP

[obj]

Apples were eaten by Joey

English Passive with agent:


137/142

S

rrr

rrr

NP VP


138/142

[nom,role:theme

agr: 1 ]

rr

rr

r

Aux

[agr: 1 ]

/be/

VP

rrr

r

V

[past-part]

eaten

PP

r

rr

P

by

NP

[obj,

role:agent]

Apples were eaten by Joey


139/142

Using features to force adjunction (English

agentless Passive):


140/142

S

rr

rrr

[case:nom] [agr: 1 ]


141/142

[role:theme]

[agr: 1 ]

NP

[tense:+]

VP

[tense:-]

[mood:past-part]

V[past-part]

eaten

[agr: 1 ]

[tense:+]

VPrr


142/142

r

rr

rr

[agr: 1 ]

Aux

/be/

VP

[tense:-]

[mood:past-part]

Apples were eaten

Rambow Slides Leysin Tag

Documents