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Dependency Representations, Grammars, Folded Structures among Other Things!. Aravind K Joshi University of Pennsylvania Philadelphia USA DEPLING, 2013 August 28 2013 Charles University, Prague, Czech Republic. Outline.

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Dependency representations grammars folded structures among other things

Dependency Representations, Grammars, Folded StructuresamongOther Things!

Aravind K Joshi

University of Pennsylvania

Philadelphia

USA

DEPLING, 2013

August 28 2013

Charles University, Prague, Czech Republic


Outline
Outline

  • How complex dependencies can be? - Projective vsNonprojective - Representations at different levels

  • Direct representations or via formal grammars

  • Empirical studies: What dependencies appear in annotated corpora and not just how often?

  • Dependencies as Folded Structures!!


Complexity of dependencies
Complexity of Dependencies

  • What do we know from the formal side?

  • Mildly Context Sensitive Languages (MCSL)-- includes Context Free Languages (CFL)

    -- Limited crossing dependencies

    -- Polynomiallyparsable


Complexity of dependencies1
Complexity of Dependencies

  • Although TAG’s (TAL’s) are in MCSL, they are much more restricted!

  • Although TAG’s support crossing dependencies, these crossing dependencies are well-nested (nested dependencies of context-free languages are well-nested—Pumping Lemma!)

    Proposed in Joshi et al. 1985 but proved only in 2010 by Kanazwa!


Complexity of dependencies2
Complexity of Dependencies

  • The language MIX, proposed by Bach (1982)as an Extreme Case of Scrambling!

  • Bach Language, MIXMIX = { w| for each n, w is a string containing n a’s, n b’s, and n c’s, in any order}Treating, each w as a ‘scrambled’ version ofn clauses, each clause containing one a, one b, and one c.

  • MIX is thus an extreme case of non-projectivity!


MIX

  • Joshi (1985) suggested that TAG’s and their variants introduced for linguisticdescriptions should not be able to generate MIX, thereby excluding it from the class of Mildly Context Sensitive Languages (MCSL)

  • Many attempts to prove this conjecture did not succeed, until very recently!

  • Kanazawa and Sylvati finally proved thisconjecture in 2012 (paper presented at ACL 2012)!


Complexity of dependencies3
Complexity of Dependencies

  • Varieties of TAG grammars all weaklyequivalent to TAG but capable of providingstructural descriptions going beyond standard TAG -- Tree Local Multicomponent TAG (MCTAG) -- Very Limited use of Set Local MCTAG -- Adequate for Scrambling and Clitic Climbing constructions (Joint work with Joan Chen Main and Tonia Bleam, 2011, 2012)


SINGLE TREE or SETS of TREESrequiring skilled tree surgeries to force a single tree over a sentence

  • Parentheticals

  • Epithets

  • Displaced adjectives, PP’s, etc.

  • Right node raising

  • Extraposition from NP

  • Sentential relatives

  • Coordinations


One treecoveringthe whole sentence

W1 W2 W3 W4 W5 W6

  • Single tree rooted in one root node

  • Every word is covered

  • All connections between the nodes are in the same dimension


Parentheticals
Parentheticals

Mary, John thinks, will win the race

Mary, John thinks, will win the race

Arterial Roots

John thinks is attached to the root node of the Mary will win the race treein an orthogonal dimension, reflecting the different semantic nature of thisattachment


Epithets
Epithets

I finished the damn book

Arterial Roots

I finished the damn book

damn is attached to the root node of the I finished the book tree an orthogonaldimension, reflecting the different semantic nature of this attachment


More examples
More examples

  • Extraposition from NP: The gardener finally came, who had the keys

  • Misplaced adjectives: An occasional sailor walked by

  • Sentential relatives:John believes* Mary will finish her dissertation this year, which no one expected her to do

    * This example is from Bonnie Webber.


Some benefits for not insisting on a single tree

  • Not going for a single tree may ease the burden on the annotators

  • Sometimes syntax does more work than necessary! Very often at the discourse annotation stage some work done by syntax has to be undone. -- Syntax should have annotated the sentence with two chunks linked in an orthogonal dimension ** It is only at the discourse annotation stage the final decision of the attachment can be made -- Striking and very frequent examples of this situation arise inATTRIBUTION


S

NP

VP

SBAR-ADV

VP

There

IN

S

have been no

Orders for the

Cray-3

NP

VP

though

S

the company

V

it is talking

With several

prospects

says

Discourse arguments

Syntactic arguments

  • There have been no orders for the Cray-3 so far, thoughthe company says it is talking with several prospects.

    • Discourse semantics: contrary-to-expectation relation between “there being no orders for the Cray-3” and “there being a possibility of some prospects”.

    • Sentence semantics: contrary-to-expectation relation between “there being no orders for the Cray-3” and “the company saying something”.


  • Attribution cannot always be excluded by default

    Advocates said the 90-cent-an-hour rise, to $4.25 an hour by April 1991, is too small for the working poor, whileopponents argued that the increase will still hurt small business and cost many thousands of jobs

    In this example, the attributing phrases stay with the argumentsof the connective while

    This decision can only be made at the discourse levelAt the sentence level two trees covering the sentence with the two trees connected in an orthogonal dimension would have been the best decision!


Annotation overview attribution in wsj
Annotation Overview: Attribution in WSJ

34% of discourse relations are attributed to an agent other than the writer.


Types of Dependencies

  • Word to Word

John loves mangoes

John bought the house

Predicate argument relation?


Types of Dependencies

Word to Phrase

John bought the house

Predicate argument relation?


Types of Dependencies

Phrase to Word

John took a walk


Types of Dependencies

Phrase to Phrase

The old man took a walk


Types ofDependencies

How much of the phrase to be included in the argument?

By convention (?) we take the maximal phrase.

John bought [the house next door which was on sale for over a year]

the house

the house next door

the house next door which was on sale for over a year

What about the minimal phrase that is sufficient to identifythe referent in the context (discourse context, for example)?


S

NP

VP

SBAR-ADV

VP

There

IN

S

have been no

Orders for the

Cray-3

NP

VP

though

S

the company

V

it is talking

With several

prospects

says

Discourse arguments

Syntactic arguments

  • There have been no orders for the Cray-3 so far, thoughthe company says it is talking with several prospects.

    • Discourse semantics: contrary-to-expectation relation between “there being no orders for the Cray-3” and “there being a possibility of some prospects”.

    • Sentence semantics: contrary-to-expectation relation between “there being no orders for the Cray-3” and “the company saying something”.


S

SBAR-ADV

NP-SBJ

VP

S

IN

MD

VP

the application

by his RGH Inc.

Although

NP-SBJ

VP

could

VB

NP

takeover

experts

VBD

SBAR

signal

said

his interest in

helping revive

a failed labor-

management bid

NP-SBJ

VP

VBD

SBAR

they

Mr. Steinberg

will make a bid

by himself

doubted

  • Althoughtakeover experts said they doubtedMr. Steinberg will make a bid by himself, the application by his Reliance Group Holdings Inc. could signal his interest in helping revive a failed labor-management bid.

    • Discourse semantics: contrary-to-expectation relation between “Mr. Steinberg not making a bid by himself” and “the RGH application signaling his bidding interest”.

    • Sentence semantics: contrary-to-expectation relation between “experts saying something” and “the RGH application signaling Mr. Steinberg’s bidding interest”.


  • Attribution cannot always be excluded by default

  • Advocates said the 90-cent-an-hour rise, to $4.25 an hour by April 1991, is too small for the working poor, whileopponents argued that the increase will still hurt small business and cost many thousands of jobs.


Do we want a single tree over a sentence?

  • There are many constructions in language that suggest that the single tree hypothesis may be wrong -- Parentheticals, supplements, sentential relatives, among others are problematic for the single tree hypothesis

Mary, John thinks, will win the election

(John thinks is attached to the S node medially but it has scope over Mary will win the election)


John heard thatMary finally finished her dissertation,which no one ever expected her to do so

( (1) John heard thatand (2) which no one ever expected her to do both have scope over (3) Mary finally finished her dissertation. Both (1) and (2) are attached to the root node S but neither (1) nor (2) have scope over the other)


Alternative lexicalization altlex
Alternative Lexicalization(AltLex)

A discourse relation is inferred between two sentences which do not contain an Explicit connective, but insertion of an Implicit connective leads to redundancy. This is because the relation is alternatively lexicalized by some non-connective expression:

  • Under a post-1987 crash reform, the Chicago Mercantile Exchange wouldn’t permit the December S&P futures to fall further than 12 points for a half hour. AltLex = (consequence)That caused a brief period of panic seeling of stocks on the Big Board.


AltLex expressions often do not correspond to syntactic constituencies.

Under a post-1987 crash reform, the Chicago Mercantile Exchange wouldn’t permit the December S&P futures to fall further than 12 points for a half hour. AltLex = (consequence)That caused a brief period of panic selling of stocks on the Big Board.

S

NP-SBJ

VP

DT

VBD

DT

PP-LOC

That

caused

a brief

period

of panic

selling…..


Syntactic Structures constituencies.

as

Folded Structures

Analogous to

Secondary or Tertiary Structures

of

Biomolecules


Biological sequences
Biological Sequences constituencies.

  • DNA, RNA, PROTEIN Sequences -- DNA and RNA: sequences of four nucleotides -- A, C, G, and T or A, C, G, and U -- Matching Pairs: A, T(U) and C, G -- Proteins: Sequences of twenty amino acids


RNA secondary structure constituencies.



Dependencies as folded structures

Dependencies as Folded Structures constituencies.

apples

John

has

eaten

John has eaten apples

apples

eaten

John

has


Dependencies as folded structures1

Dependencies as Folded Structures constituencies.

John

has

eat

en

apples

John has eaten apples

apples

eat

John

has

en


Subject relatives

Subject Relatives constituencies.

The cat that chased the rat fled

NP1 that V1 NP2 V2

V2

NP1

that

V1

NP2


Object relatives

Object Relatives constituencies.

Goes into another plane and comes out

The cat that the dog chased fled

NP1 that NP2 V2 V1

NP2

V1

NP1

that

V2

Object Relatives are more complex than

Subject Relatives, even at the first level.


Crossing versus nested dependencies

Crossing constituencies.

Crossing Versus Nested Dependencies

Both (1) and (2) can be folded in one plane!

(1’) NP1 NP2 V2 V1

(2’) NP1 NP2 NP3 V3 V2 V1

Nested

(1) NP1 NP2 V1 V2

(2) NP1 NP2 NP3 V1 V2 V3

(1’)canbe folded in one plane.

(2’)cannotbe folded in one plane.

Beyond 2 levels of embedding this difference disappears!!

cf Bach and Marslen Wilson (1985)



Pseudoknots in linguistic structures
Pseudoknots in Linguistic Structures constituencies.

N1 N2 N3 V3 V2 V1

Move N2 N3 V3 V2 to the right of V1 and then

Move N2 N3 back

N1 V1 N2 N3 V2 V3

N1 N2 N3 V1 V3 V2


Pseudoknots in linguistic structures1
Pseudoknots In Linguistic Structures constituencies.

N1 N2 N3 V1 V3 V2

Remnant Extraposition

N1 V1

N2 N3

V2 V3

FOLDED STRUCTURE AS THE SYNTACTIC STRUCTURE!!


Folded Structures as Syntactic Structures constituencies.

When Subject Relatives and Object Relatives are

represented as Folded Structures, Object Relatives

are more costly than Subject Relatives

-- even at the first level of embedding!

Object Relatives require going out of the plane and

coming back up as in the case of parallel strands!

Optimization with respect to Folded Structures !!


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