history of major nlp products & services in toshiba

1 / (プレゼンテーション資料の作り方ご提案） 1Toshiba Confidential TOSHIBA OF EUROPE LTD.

History of Major NLP Products & Services in Toshiba

１９７８「 JW-10 」： Japanese Word Processor

１９８５「 ASTRANSAC EJ 」 : EtoJ MT System

１９８９「 ASTRANSAC JE 」 : JtoE MT System

１９９５「 The 翻訳」： PC MT System (Internet & Personal)

１９９６「 News Watch 」 : Information Filtering Service

１９９９「 Fresh Eye 」： Internet Search Engine/Portal

２００１「 KnowledgeMeister 」： KM Support System

２００５　　 Chinese-Japanese Translation Service

２００６　　「 KnowledgeMeister - Succeed 」

2

Confidential

00 Month 0000 (edit in View > Header and Footer)

2Toshiba Confidential 2TOSHIBA OF EUROPE LTD.

Toshiba of Europe Ltd.Hideki Hirakawa

Integrated Use of Phrase Structure Forest and Dependency Forest in Preference Dependency Grammar (PDG)

29 January, 2008

3 3Toshiba Confidential TOSHIBA OF EUROPE LTD.

Agenda

Phrase Structure and Dependency Structure Analysis

Overview of the Preference Dependency Grammar(PDG)

Packed Shared Data Structure “Dependency Forest”

Evaluation of Dependency Forest

Conclusion


Phrase Structure (PS) and Dependency Structure (DS)

Two major syntactic representation schemes

detpre

vp

n

time fly like an arrow

nv

np

pp

np

s

Information explicitly expressed by PS- Phrases (non-terminal nodes)- Structural categories (non-terminal labels)

detpre

vppsub

time fly like an arrow

Information explicitly expressed by DS- Head-dependent relations (directed arcs)- Functional categories (arc labels)

Phrase Structure (PS) Dependency Structure (DS)


Constituency and dependency describe different dimensions.

A phrase-structure tree (PST) is closely related to a derivation, whereas a dependency tree rather describes the product of a process of derivation.

Constituency and dependency are not adversaries, they are complementary notions. Using them together we can overcome the problems that each notion has individually.

Formal & Computational Aspects of Dependency Grammar [Kruijff 02]

Relation between PS (Constituency) and DS


Phrase structure analysis - Lexicalized PCFG Lexical information (including dependency relation) improves PS analysis accuracy (ex. Charniak 1997; Collins 1999; Bikel 2004)

- Use of dependency relations as discriminative features of maximum entropy phrase structure parser (ex. HPSG Parser (Oepen 2002), Reranking parser (Charniak and Johnson 2005))

- Use of another independent shallow dependency parser (Sagae et al. 2007)

Dependency analysis Almost no use of phrase structure information (Kakari-uke parsers, MSTParser (McDonald 2005), Malt parser(Nivre 2004)

Integration requires mapping Integration of PS and DS requires mapping between two structures of a sentence because sentence analyzers cannot combine any linguistic information without correspondence between the two structures.

Integrated Use of Phrase and Dependency Structures


Mapping between PS and DS （ traditional researches ） Conversion from/to PS to/from DS based on heuristics 　　 Phrase Structure Tree (PST) → Dependency Tree (DT) [Collins 99],　　 DT → PST [Xia&Palmer 00] 　 ⇒ Measurement of parse accuracy, tree bank creation etc.

Grammar equivalence　　 [Gaifman 65],[Abney 94] studied the equivalence relation between CFG PSG (CFG) and DG (Tesniere model DG)　　⇒　 DG is strongly equivalent to only sub-class of CFG*1

Structure mapping based on packed shared data structures Partial structure mapping framework based on the Syntactic Graph [Seo&Simmons 89]. 　　 Creates mappings between PSTs and DTs based on partial structure mapping rules (described later) 　⇒ Syntactic graph generates inappropriate mapping [Hirakawa 06]

Complete mapping based on the “Dependency Forest”⇒ Integrated use of PS and DS (described later)


Agenda





Conclusion


Basic Sentence Analysis Model

Sentence ◎○○

×

×

×

×

× ×

×

○

×

Generation Knowledge generates all possible interpretations

Interpretation Space　 prescribed by interpretation description scheme

Constraint Knowledge rejection of interpretations

Preference Knowledge preference order of interpretations

○

Interpretation ◎ correct

○ plausible× implausible

◎ ○ ×＞＞

Optimum Interpretation Extraction

◎The optimum interpretation

reject

accept


Example （１） Probabilistic Context Free Grammar(PCFG)

◎○○

×

×

×

×

××

×

○

×○

Generation Knowledge CFG rules

Interpretation Space　 Phrase structure (parse tree)

Constraint Knowledge No constraints

Optimum Interpretation Extraction the Viterbi algorithm

Preference Knowledge Probabilities of the CFG rules

◎ ○ ×＞＞

Sentence ◎The optimum interpretation


Basic Sentence Analysis Model of PDG

PK: Preference Knowledge, CK: Constraint Knowledge, GK: Generation Knowledge, IS: Interpretation Space

(a) NLA system with multilevel interpretation space(b) Packed shared data structure and interpretation mapping(c) Interpretations are externalizations of the lower level interpretations

Multilevel Packed Shared Data Connection Model

PK1 CK1

Sentence

GK1

IS1

5◇

◇

3◇

IS2

◎The OptimumInterpretation

OptimumInterpretationExtraction

mapping

2◇△

2△

△

△

4△

△ △

5△

6△

m△

3△

△ 1△△

Level 1 Interpretation:

IS3

△△ ◎◇◇

l ◇

◇◇

◇

4◇1◇◇

◇

◇

△

△

PK2 CK2

GK2

PK3 CK3

GK3

2○n○

5○1◎○

○

4○3○

6○

○

○

Level 2 Interpretation: Level 3 Interpretation:

1. Data Structure2. Optimum Solution Search


PDG Implementation Model (data structure)

WPP = Word POS Pair, Phrase structure forest (PSF) = (packed shared) parse forest

Syntactic Layer

○○

All PSTs All DTs

Sentence“Time flies”

Morphological Layer

The OptimumDependency Tree

○

All WPP sequences

Interpretation mapping

Phrase str. forest

np np vp

fly/v

time/n

time/v

fly/n

vp

roots s

Dependency forest

top

fly/v

time/n

time/v

fly/n

obj

sub

toptopfly/vtime/n

time/v fly/n

WPP trellis

△△

×

××

×

×

top

fly/v

time/nsub

topDTPST

np vp

time/n fly/v

rootsWPP sequence

fly/vtime/n

PDG is an all-pair dependency analysis method with three level architecture utilizing three packed shared data structures

Integrated use of PS and DS level in syntactic layer


□

： △

△

×

： ◎Optimum interpretation

1 □2 ◎

1 ◎

： ◎Optimum interpretation

□ 2 ◎

1 ◎

：

MSTParser

PDG

All MorphologicalInterpretations

1-best MorphologicalInterpretation

No CFG Grammar

MorphologyLevel

All DS Interpretations

All Interpretations with no POS ambiguities

◎

◎

：Well-formed Interpretations

Sentence ◎ , ◎

Comparison with other dependency analysis methods

No CFG Grammar

Sentence

Sentence

All DS Interpretations

PS Level DS Level

CDG

All PS Interpretations

CFG Filtering

CDG: Constraint Dependency Grammar, MSTParser : Maximum Spanning Tree Parser

CombinatorialExplosion

Over Pruning


PDG Implementation Model (optimum solution search)

Integration of Preference Knowledge: Preference scores based on multilevel data structures are integrated into scores on a DF

Scoring

“Time flies”

Graph Branch Algorithm

PS forest

np np vp

fly/v

time/n

time/v

fly/n

vp

roots s

Dep. forest

top

fly/v

time/n

time/v

fly/n

obj

sub

toptopSentence fly/vtime/n

time/v fly/n

WPP trellis

The optimum dep. tree

Score integration

WPP seq. score Phrase str. score Dep. score

top

time/n

top

subfly/v

Syntactic LayerMorphological Layer

Optimum solution search


PDG Analysis FlowSentence

Dependency Forest

PS Forest

WPP Trellis

Scored Dependency Forest

Extended Chart Parser

Forest Generation Scoring

Optimum TreeSearch

・ Preference Score Integration

・ Optimum Tree Search based on CM and PM

The Optimum Tree

Co-occurrenceScore Matrix

・ Dependency Forest Generation


Agenda





Conclusion


＝

Grammar Rule : partial structure mapping rule

X1/w1

Y/wh

Xh/wh Xn/wnXi/wi… … …

whd1 di

w1widn

wn

…

…

Partial Dependency Tree

Parser

Mapping

Sentence

Set of dependency trees

◇

◇

◇◇

◇◇

◇◇

◇◇◇

◇◇◇ ◇

◇

△△

△

△△

△△

△△

△ △

△△ △

△

＝

Mapping

Set of phrase structure trees

Packed Shared Dependency Structure(Syntactic Graph)

Packed Shared Phrase Structure(Phrase structure forest)

Partial Structure Mapping Method [Seo&Simmons 89]

Headed CFG Rule


Syntactic Graph

Packed Shared Data Structure for Dependency Trees

Encompasses all dependency trees corresponding to phrase structure trees in the parse forest for a sentence

[1,fly,v][0,time,n]

[0,time,v] [1,fly,n]

[2,like,p]

[2,like,v]

[3,an,det] [4,arrow,n]

mod npp vnp

det

ppnvpp

vppsnp

snpvnp

SS

S

“Time flies likes an arrow”

1 2 3 4 5 6 7 8 9 10 11 12 13

1 1 1 1 1 1 1 1 1

2

3 1 1 1 1

4 1 1 1 1 1 1 1 1

5 1 1 1 1 1 1 1 1

6 1 1 1 1 1 1 1 1

7 1 1 1 1 1 1 1 1

8 1 1 1 1 1 1 1 1

9 1 1 1 1 1 1 1

10 1 1 1 1 1 1 1 1

11 1 1 1 1 1 1 1

12 1 1 1 1 1 1 1 1

13 1 1 1 1 1 1 1 1

Node: WPP Arc: Dependency Relation

Syntactic Graph Exclusion Matrix


Completeness and Soundness of the syntactic graph

Definitions

Completeness ： For every parse tree in the forest, there is a syntactic reading from the syntactic graph that is structurally equivalent to that parse tree.

∀PST ： Phr.Str.Tree 　∃ＤＴ： Dep.Tree PST corresponds to DT

Soundness ： For every syntactic reading from the syntactic graph, there is a parse tree in the forest that is structurally equivalent to that syntactic reading.

∀ ＤＴ： Dep.Tree 　 ∃ PST ： Phr.Str. Tree PST corresponds to DT

Problem of the syntactic graph Violation of the soundness [Hirakawa 06]

×

×

×○

○

○

○

Phrase structure forestSyntactic graph

completeness

soundness

×

Dep. tree ： DT Phr. str. tree ： PT


Example of the violation of soundness

Tokyo taxi driver call center○ ○ ○ ○ ○

nc-1 nc-2 nc-6nc-3

nj-5 nj-7S

rt-8nj-4

np1

Tokyo taxi driver call center

○ ○ ○ ○ ○

nc-1 nc-2 nc-6

nj-7S

rt-8

np3


nc-1 nc-6nc-3nj-5

Srt-8

np2


nc-2 nc-6nc-3nj-4

S

rt-8

○ ○ ○ ○ ○

nc-1 nc-2 nc-6nc-3

(a) (b)

(c)

(d)

Syntactic graph for (a),(b) and (c) generates (d) which has no corresponding phrase structure tree in the phrase structure forest

EM 1 4 2 5 3 7 6 81 14 1 1 12 15 1 1 13 17 1 1 168

Syntactic Graph/Exclusion Matrix

S

rt-8


Packed Shared Data Structure for Dependency Trees

Dependency Forest(DF) = Dependency Graph(DG) + Co-occurrence Matrix(CM) CM(Dependency Forest): Defines the arc co-occurrence relation (Equivalent arcs are allowed in DF)

Dependency Forest [Hirakawa 06]

Co-occurrence MatrixDependency Graph

Dependency Forest for “Time flies like an arrow.”

npp19

det14

pre15vpp20

vpp18sub24

sub23obj4

nc2 obj16

0,time/n 1,fly/v

0,time/v 1,fly/n

2,like/p

2,like/v

3,an/det 4,arrow/n

root

rt29

rt32

rt31

2 24 4 23 19 18 20 14 16 15 31 29 322 － ○ ○ ○ ○ 24 － ○ ○ ○ ○ 4 － ○ ○ ○ ○ ○23 ○ － ○ ○ ○ 19 ○ － ○ ○ ○18 ○ － ○ ○ ○ 20 ○ － ○ ○ ○14 ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○16 ○ ○ ○ － ○ 15 ○ ○ ○ ○ ○ ○ － ○ ○31 ○ ○ ○ ○ － 29 ○ ○ ○ ○ － 32 ○ ○ ○ ○ ○ －

obj25


Features of the Dependency Forest

Mapping is assured (phrase structure tree ⇔ dependency tree) → usable for multilevel packed shared data connection model

High flexibility in describing constraints

　　 ex. non-projective dependency structure*1

*1 : dependency structure violating at least the following projectivity conditions ''no cross dependency exits'' ''no dependency covers the top node''


Generation Flow of Phrase Structure Forest and Dependency Forest

Input sentence

WPP Trellis

Parse Forest

Initial Dependency Forest

DF Extraction

Chart Parsing

Dictionary

ExtendedCFG

Optimum Solution Search

Dependency Tree

Dependency Forest

Morphological Analysis

DF Reduction

(1)

(2)

(3)

(4)

PDG analysis process PDG data structure


y/Xi→x1/X1,...,xn/Xn

CFG 　　

PDG Grammar RuleExtended　CFG　rule　with　phrase　head　and　mapping　to　dependency　structure

Xi: Variable Xh(phrase head) :

　　　“ Xh” is either of “X1”..“Xn”

Rewriting rule part

y/X ｈ→ x1/X1,...,xn/Xn

Dependency tree

Nodes: X1, ... , Xn

Top node: Xh

: [arc(arcname1,Xi,X ｊ ),...,arc(arcnamen-1,Xk,X ｌ )] Dependency structure part

　ex.　　vp/V　→　v/V,　np/NP,　pp/PP　　　　　:　　　[arc(obj,NP,V),　arc(vpp,PP,V)]

V （ = see/v ）

obj

PP （ = in/pre ）NP （ = girl/n ）

vpp

vp/V(=see/v)

v/V(=see/v) np/NP(=girl/n)pp/PP(=in/pre)

Phrase structure Dependency structuresee a girl in the forest


Standard Chart Parsing: Structure of Standard Edge

<0,2, s　→ np　　・ vp　pp>

a cat chases …

0 1 2 3

<0,2, np　→ det　noun　・>

<0,1,det　→ [a]・> <1,2,n　→ [cat]・> <2,3,v　→ [chase]・>Lexical edge

Inactive edge

Active edge

Input position

EDGE <0,2, s → np・ vp pp>

Startposition

Endposition

Head category

Found constituents Remainingconstituents


Structure of PDG Edge

a cat chases 0 1 2 3

<0,1,det → [a] ・ : [a-det-0]>

<0,2, s/V → np/[cat-n-1] ・ vp/V pp/PP : [arc(obj,/[cat-n-1],V), arc(vpp,PP,V)]>

Two extensions to the standard edge structure

(1) Mapping to dependency structure

(2) Packing of inactive edges　　　　　　　PDG (packed) edge is a set of sharable PDG single edges

<0,2, np/[cat-n-1] → det/[a-det-0] noun/[cat-n-1] ・ : arc(det,[a-det-0] ,[cat-n-1] )>

PDG single edge = Standard edge + Phrase head + Dependency structure(tree)

<1,2,n → [cat] ・ : [cat-n-1]> <2,3,v → [chase] ・ : [chase-n-2]>


・ Bottom-up chart parser using the Agenda

・ Terminates when the Agenda becomes empty

Generation of Phrase Structure Forest and Initial Dependency Forest

Chart

Agenda

φ

Inactive Edges

Active edges<E12 s2→… … ・><E12 s2→... … ・> ：

<E52 np2→... ・ > ：

<E1 s1 　→　 [[np1 vp1]][ds11 ] >

<Eroot root 　→　 [s1 s2][ds1 ds2]>

<E2 s2 →…>

<E3 np1 　→　 [[det1 n1]]: [ds31 ] >

<E4 vp1 　→　 [[v1 np2] [v1 np3 pp1]]: [ds41 ds42] >

<Er root→[s] ・><E2 s2 →… ・ > ：

Phrase Structure Foresta set of inactive edges reachable from the root edge

Initial Dependency Graph a set of arcs in the PS forest

arc(root-17,[like]-v-2,[root]-x),arc(root-24,[flies]-v-1,[root]-x),arc(root-27,[time]-v-0,[root]-x),arc(sub-16,[flies]-n-1,[like]-v-2),arc(nc-4,[time]-n-0,[flies]-n-1),arc(obj-14,[arrow]-n-4,[like]-v-2),　　　　　　　　　　　　　：

<E3 np1 　→　・

・・ >Arc3,..

<E4 vp1 　→　・・・ >

Arc8,Arc9,..

Initial Co-occurrence Matrix CM1 ～ 3:CMatrix setting condition

<E1 s1 　→　 [[np1 vp1 pp1]][Arc1,Arc2 ]

>

A1 A2 A3 A4 A8 A9

A1

A2

A3

A4

A8

A9

CM1: Between arcs in DS

○○

CM2: Between arcs in DS and arcs governed by constituents ○

○○

○

○ ○○○

○ ○○○

CM3: Between arcs governed by different constituents

○ ○

○

○


Generation of Phrase Structure Forest and Initial Dependency Forest

Chart

Agenda

φ

Inactive Edges

Active edges<E12 s2→… … ・><E12 s2→... … ・> ：

<E52 np2→... ・ > ：

<Er root→[s] ・><E2 s2 →… ・ > ：

Phrase Structure Forest

Initial Dependency Graph a set of arcs in the PS forest

Initial Co-occurrence Matrix CM1 ～ 3:CMatrix setting condition

2 24 4 25 23 19 18 20 14 16 15 31 29 322 － ○ ○ ○ ○ 24 － ○ ○ ○ ○ 4 － ○ ○ ○ ○25 － ○ ○ ○ ○23 ○ － ○ ○ ○ 19 ○ － ○ ○ ○18 ○ － ○ ○ ○ 20 ○ － ○ ○ ○14 ○ ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○16 ○ ○ ○ － ○ 15 ○ ○ ○ ○ ○ ○ ○ － ○ ○31 ○ ○ ○ ○ － 29 ○ ○ ○ ○ － 32 ○ ○ ○ ○ ○ ○ －

Initial Dependency Forest

178 np

[1,fly,v][0,time,n]

[0,time,v] [1,fly,n]

[2,like,p]

[2,like,v]

[3,an,det]

[4,arrow,n]

123 np103 np

166

169

150

153

138

121110

101

133 np

184 vp188 pp

197 np189 vp

201 vp195 vp

191 s 186 s196 s

186 root


Reduction of the Initial Dependency Forest

npp19

vpp18sub24

sub23obj4

nc2

obj25

0,time/n 1,fly/v

0,time/v 1,fly/n

2 24 4 25 23 19 18 20 14 16 15 31 29 322 － ○ ○ ○ ○ 24 － ○ ○ ○ ○ 4 － ○ ○ ○ ○25 － ○ ○ ○ ○23 ○ － ○ ○ ○ 19 ○ － ○ ○ ○18 ○ － ○ ○ ○ 20 ○ － ○ ○ ○14 ○ ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○16 ○ ○ ○ － ○ 15 ○ ○ ○ ○ ○ ○ ○ － ○ ○31 ○ ○ ○ ○ － 29 ○ ○ ○ ○ － 32 ○ ○ ○ ○ ○ ○ －

Equivalentarc

Generated　from　two　grammar　rules　vp/V　→ v/V,np/NP　　　　　　　:　[arc(obj,NP,V)]　vp/V　→ v/V,np/NP,pp/PP　:　[arc(obj,NP,V),　　　　　　　　　　　　　　　arc(vpp,PP,V)]

npp19

vpp18sub24

sub23obj4

nc2

0,time/n 1,fly/v

0,time/v 1,fly/n

Reduction

2 24 4 23 19 18 20 14 16 15 31 29 322 － ○ ○ ○ ○ 24 － ○ ○ ○ ○ 4 － ○ ○ ○ ○ ○23 ○ － ○ ○ ○ 19 ○ － ○ ○ ○18 ○ － ○ ○ ○ 20 ○ － ○ ○ ○14 ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○16 ○ ○ ○ － ○ 15 ○ ○ ○ ○ ○ ○ － ○ ○31 ○ ○ ○ ○ － 29 ○ ○ ○ ○ － 32 ○ ○ ○ ○ ○ －

more than one equivalent arc is merged into one arc without increasing the number of the generalized dependency trees in the dependency forests


Completeness and Soundness of the Dependency Forest

Completeness : All phrase structure trees in the parse forest have corresponding dependency trees in the dependency forest.

∀PT： phrase　structure　tree 　 ∃ DT： dependency　tree 　dep_tree(PT)　=　DT

Soundness ： Every phrase structure tree corresponding to a dependency tree in the dependency forest exists in the phrase structure forest

∀DT： dependency　tree 　　∃ PT： phrase　structure　tree 　 dep_tree(PT)　=　DT

×

×××

×

○

○ ○

○○

×DT ： dependency tree PT ： phrase structure tree

Phrase structure forestDependency forest

○

○

1:N correspondence in general

The completeness and soundness of the dependency forest is assured [Hirakawa 06]


Evaluation of the Dependency Forest Framework

Analysis of prototypical ambiguous sentences

1 to N / N to 1 correspondence between phrase structure tree/trees and dependency trees/tree

Generation of Non-projective dependency tree


=========== s/Sentence =========== (R1)　s/VP→　np/NP,vp/VP　:　[arc(sub,NP,VP)]　%　Declarative　sentence (R2)　s/VP→　vp/VP　:　[]　%　Imperative　sentence========= np/Noun　Phrase ======== (R3)　np/N→　n/N　:　[]　%　　Single　noun (R4)　np/N2→　n/N1,n/N2　:　[arc(nc,N1,N2)]　　　　%　Compound　noun (R5)　np/N→　det/DET,n/N　:　[arc(det,DET,N)]　 %　 (R6)　np/NP→　np/NP,pp/PP　:　[arc(npp,PP,NP)]　%　Prepositional　phrase　attachment (R7)　 np/N→　 ving/V,n/N　 :　 [arc(adjs,V,N)]　 %　 Adjectival　usage（ subject） (R8)　 np/N→　 ving/V,n/N　 :　 [arc(adjo,V,N)]　 %　 Adjectival　usage（ object） (R9)　np/V→　ving/V,np/NP　:　[arc(obj,NP,V)]　 %　Gerund　phrase(R10)　 np/V→　 ving/V,np/NP,pp/PP　 :　 [arc(obj,NP,V),arc(vpp,PP,V)]　 %　 Gerand　phrase　with　PP(R11)　 np/NP→　 np/NP0,and/AND,np/NP:　 [arc(and,NP0,NP),arc(cnj,AND,NP0)]%　Coordination　(and)(R12)　 np/NP→　 np/NP0,or/OR,np/NP　 :　 [arc(or,NP0,NP),arc(cnj,OR,NP0)]　　　 %　Coordination (or)=========　vp/Verb ======== phrase(R13)　vp/V→　v/V　:　[]　 %　Intransitive　verb(R14)　vp/V→　v/V,np/NP　:　[arc(obj,NP,V)]　 %　Transitive　verb(R15)　vp/V→　be/BE,ving/V,np/NP　:　[arc(obj,NP,V),arc(prg,BE,V)]　%　Progressive(R16)　vp/BE→　be/BE,np/NP　:　[arc(dsc,NP,BE)]　　%　Copular(R17)　vp/VP→　vp/VP,pp/PP　:　[arc(vpp,PP,VP)]　　%　PP-attachment(R18)　vp/VP→　adv/ADV,vp/VP　:　[arc(adv,ADV,VP)]　　%　Adverb　modification(R19)　vp/V→　v/V,np/NP,adv/ADV,relc/RELP 　%　non-projective　pattern　　　　　　　 :[arc(obj,NP,V),arc(adv,ADV,V),arc(rel,RELP,NP)]　========　pp/Prepositional　phrase ========(R20)　pp/P→　pre/P,np/NP　:[arc(pre,NP,P)]　

Grammar rules for typical ambiguities (PP-attachment,Coordination, be-verb usage)

Grammar for Ambiguous Sentences


PP-attachment Ambiguity

Input sentence: I saw a girl with a telescope in the forest.

Five well-formed dependency trees

0,I 1,saw 2,a

root

4,with 6,telescope 8,the 9,forest3,girl 5,a 7,in

det4,0det11,0 det42,0

sub33,20 obj6,20

vpp16,15

vpp27,5

npp14,10pre12,10 pre24,10

npp29,5

npp26,5root23,0

Node0,I : [i]-n-01,saw : [saw]-v-12,a : [a]-det-23,girl : [girl]-n-34,with : [with]-pre-45,a : [a]-det-56,telescope : [telescope]-n-67,in : [in]-pre-78,the : [the]-det-89,forest : [forest]-n-9root : [root]-x-root

33 4 6 14 16 11 12 29 26 27 23 24 4233 － ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○4 ○ － ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○6 ○ ○ － ○ ○ ○ ○ ○ ○ ○ ○ ○ ○14 ○ ○ ○ － ○ ○ ○ ○ ○ ○ ○ ○16 ○ ○ ○ － ○ ○ ○ ○ ○ ○ ○11 ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○ ○ ○12 ○ ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○ ○29 ○ ○ ○ ○ ○ ○ － ○ ○ ○26 ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○27 ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○23 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ － ○ ○24 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ － ○42 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ －

Crossing

Single role


Coordination Scope Ambiguity

Input sentence ： Earth and Moon or Jupiter and Ganymede.

Node0,earth : [earth]-n-01,and : [and]-and-12,moon : [moon]-n-23,or : [or]-or-34,jupiter : [jupiter]-n-45,and : [and]-and-56,ganymede: [ganymede]-n-6root : [root]-x-root

0,earth 1,and 2,moon 3,or 4,jupitor

root

5,and 6,ganymede

and12,10

and25,20

cnj2,0

or9,4

cnj6,0

or22,3

cnj14,0

and18,12

root26,0

and14,5

25 12 4 2 22 9 6 18 14 2625 － ○ ○ ○ ○ ○ ○ ○12 － ○ ○ ○ ○ ○ ○4 － ○ ○ ○ ○ ○ ○ ○2 ○ ○ ○ － ○ ○ ○ ○ ○ ○22 ○ ○ ○ － ○ ○ ○ ○9 ○ ○ ○ ○ － ○ ○ ○ ○6 ○ ○ ○ ○ ○ ○ － ○ ○ ○18 ○ ○ ○ ○ ○ ○ ○ － ○ ○14 ○ ○ ○ ○ ○ ○ ○ ○ － ○26 ○ ○ ○ ○ ○ ○ ○ ○ ○ －

Crossing

Single role

Five well-formed dependency trees


Structural Interpretation Ambiguity andPP-attachment Ambiguity

Input sentence: My hobby is watching birds with telescopeTen well-formed dependency trees

0,my 1,hobby 2,is 3,watching 4,birds

root

5,with 6,telescope

sub35,1

sub38,10

prg2,10adj4,12

dsc33,8

dsc36,10

obj6,15

sub5,5

npp23,5

npp27,3

vpp24,7

root44,0det1,0 pre22,0root41,0

Node0,my : [my]-det-01,hobby : [hobby]-n-12,is : [is]-be-23,watching : [watching]-ving-34,birds : [birds]-n-45,with : [with]-pre-56,telescope : [telescope]-n-6root : [root]-x-root

1 38 35 2 4 33 36 6 5 23 27 24 22 44 411 － ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○38 ○ － ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ 35 ○ － ○ ○ ○ ○ ○ ○2 ○ ○ － ○ ○ ○ ○ ○4 ○ ○ － ○ ○ ○ ○ 33 ○ ○ － ○ ○ ○ ○ ○ ○ ○ 36 ○ ○ ○ － ○ ○ ○ 6 ○ ○ ○ ○ ○ － ○ ○ ○ ○ ○ ○5 ○ ○ ○ － ○ ○ ○ ○ 23 ○ ○ ○ ○ ○ ○ ○ ○ ○ － ○ ○ ○27 ○ ○ ○ ○ ○ － ○ ○ 24 ○ ○ ○ ○ ○ ○ － ○ ○ ○22 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ － ○ ○44 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ － 41 ○ ○ ○ ○ ○ ○ ○ －


N to 1 Correspondence from PSTs to One DT （ 1 ）

Spurious ambiguity (Eisner96),(Noro05)

　 (R17)vp/VP→　 vp/VP,pp/PP　　 :　 [arc(vpp,PP,VP)] 　 %　PP-attachment

　 (R18)vp/VP→　adv/ADV,vp/VP　:　[arc(adv,ADV,VP)]　 %　Adverb　modification

in the forestsaw a catShe curiously

vp ppnp adv

vp

vp

s

in the forestsaw a catShe curiously

vp ppnp adv

vp

vp

sRule application: R17 → R18 Rule application: R18 → R17

sawShe in the foresta catcuriously

adv

vpp


Modification scope problem (Mel'uk88)　 Dependency structure has ambiguities in modification scope when it has a head word which

has dependants located at the right-hand side and the left-hand side of the head word.　　 ex. Earth and Jupiter in Solar System.

0,Earth 1,and 2,Jupiter 3,in 4,Solar System

rootand4,20npp8,0

4 2 8 7 124 － ○ ○ ○ ○2 ○ － ○ ○ ○8 ○ ○ － ○ ○7 ○ ○ ○ － ○12 ○ ○ ○ ○ －

pre7,0cnj2,0 root12,0

・ Introduction of “Grouping” （ Coordination and operator words (ex. not, only) ） [Mel'uk88]

・ Japanese has no modification scope problem because it has no right to left dependency.

Jupiter

np

in Solar System

pp

Earth

np

and

cnj

np

np

Jupiter

np

in Solar System

pp

Earth

np

and

cnj

np

np

N to 1 Correspondence from PSTs to One DT （ 2 ）


Generation of Non-projective Dependency Tree

Grammar　rule　for　non-projective　dependency　tree　

　 (R19)vp/V　→　v/V,np/NP,adv/ADV,relc/REL 　　　　　　 :

[arc(obj,NP,V),arc(adv,ADV,V),arc(rel,RELP,NP)]

1,saw 2,the

root

5,which was Persian0,She 4,curiously3,cat

det4,0sub12,20

obj6,20

adv10,15

root14,0

re1 11,10

12 4 6 10 11 1412 － ○ ○ ○ ○ ○4 ○ － ○ ○ ○ ○6 ○ ○ － ○ ○ ○10 ○ ○ ○ － ○ ○11 ○ ○ ○ ○ － ○14 ○ ○ ○ ○ ○ －

Input sentence ： She saw the cat curiously which was Persian*1

*1: Artificial example for showing the rule applicability


Conclusion

Dependency forest is a packed shared data structure - Bridge between phrase structure and dependency structure

usable for Multilevel Packed Shared Data Connection MODEL of PDG

- High flexibility in describing constraints

　　　　　　　　　　　　　　 Future work

Extension of the framework for the modification scope problem (Grouping)

Real-world system implementation

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