Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

69
Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró

Transcript of Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Page 1: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Semantic Parsing based on Propositional Representations

Fernandez, Sopena, Padró

Page 2: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

• Resumen– Objetivos– Estructura Proposicional– Arquitectura

• M1• M2

– Ejemplos• Canónico• PP-Attachment

Generalized Role Labeling using Propositional Representations

Page 3: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

• Objetivos

• Crear un analizador semántico mediante la implementación de un modelo psicológico plausible que:

– Lleva a cabo un mapeo directo i sencillo de las frases a su estructura proposicional

– No utiliza analizadores sintácticos ni estructura sintactica intermedia

• Obtener buenos resultados en textos reales (PTB)

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Generalized Role Labeling using Propositional Representations

• Estructura proposicional– Predicado + 3 ArgumentosFrase canónica: “The man sold some offerings to the british tourist”

Pred:sold

Arg1:the man

Arg2:some offerings

Arg3:the british tourist

– Composición de proposiciones“The man sold some offerings to the british tourist in Barcelona”

(P1)Pred:soldArg1:the manArg2:some offeringsArg3:the british tourist

(P2)Pred:Arg1:P1Arg2:in BarcelonaArg3:

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Generalized Role Labeling using Propositional Representations

Page 6: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

• Estructura proposicional

– Argumentos temáticos generalizados (VanValin)

• A1-ACTOR (agent, perceiver, ....)

• A2-UNDERGOER (theme, patient, ... )

• A3-OTHERS (benefactive, goal, location, source, destination, ...)

– Mapping fácil con FrameNet i otros

• Arg1 i arg2 son los dos primeros argumentos core

• Arg3, argumentos core que se identifican por la preposición que los marca.

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• Arquitectura• Modulo1 - Estructural/Sintáctico

– Lleva a cabo el mapeo directo de las palabras a la proposición

– Modifica la proposición

– Sin información semántica explicita

• Modulo2 - Semántico– Acepta o rechaza las decisiones estructurales del primer

modulo.

» Consistencia con el verbo (+/-Subcategorization Frames)

» PP-Attachment

» Coordinacion

» Relativo

Generalized Role Labeling using Propositional Representations

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Ventajas

• Complejidad lineal

• Fácil tratamiento de fenómenos sintácticos ‘difíciles’:– Coordinación y puntuación.– Word order– Non local dependencies.

• No se necesita corpus sintáctico etiquetado.

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• Arquitectura– Modulo1

Input Word

Slot 0 Slot 1 Slot 2 Slot 3 Type S Back, Test & Subcat.

STACK

Stored Context

Current Context

MODULE 1MODULE 1

Parser Commands

Page 10: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Input Word

Slot 0 Slot 1 Slot 2 Slot 3 Type S Back, Test & Subcat.

Current Context

MODULE 2MODULE 2

Verbo|ARG Adjunto|~Adjunto Coordinable|~CoordinableArg1|Arg2

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2PUT1

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

The | DT

PUT1

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

The | DT

DT(The)

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

man | DT_N

DT(The)

PUT1

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

DT(The) DT_N(man)

man | DT_N

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

sold | V_MA

PUT0

DT(The) DT_N(man)

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

DT(The) DT_N(man)

sold | V_MA

V_MA(sold)

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

some | DT

PUT2

DT(The) DT_N(man)V_MA(sold)

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

V_MA(sold)

some | DT

DT(The) DT_N(man) DT(some)

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Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Flags

Modul1 Modul2

The man sold some offerings to the president

offerings | DT_N

PUT2

Slot 3V_MA(sold) DT(The) DT_N(man) DT(some)

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Flags

Modul1 Modul2

The man sold some offerings to the president

offerings | DT_N

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings)

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Flags

Modul1 Modul2

The man sold some offerings to the president

to | IIN_DT

PUT3

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings)

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Flags

Modul1 Modul2

The man sold some offerings to the president

to | IIN_DT

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings) to(IIN_DT)

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Flags

Modul1 Modul2

The man sold some offerings to the president

the | DT

PUT1

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings) to(IIN_DT)

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Flags

Modul1 Modul2

The man sold some offerings to the president

the | DT

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings) to(IIN_DT) the(DT)

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Flags

Modul1 Modul2

The man sold some offerings to the president

president | DT_N

PUT1

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings) to(IIN_DT) the(DT)

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Flags

Modul1 Modul2

The man sold some offerings to the president

president | DT_N

Slot 3

Slot 0 Slot 1 Slot 2 Slot 3

Slot 3V_MA(sold) DT(The) DT_N(man) DT(some) DT_N(offerings) to(IIN_DT) the(DT)

president(DT_N)

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Generalized Role Labeling using Propositional Representations

Un ejemplo no tan sencillo:

“The main manager bought some old cars with three wheels.”

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Generalized Role Labeling using Propositional Representations

Current

Pred:

A1:

A2:

A3:

Flags:

Input Word: The| DTM1: PUT1M2:

The main manager bought some old cars with three wheels.

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Generalized Role Labeling using Propositional Representations

Current

Pred:

A1: The

A2:

A3:

Flags: @1

Input Word: The| DTM1: NEXTM2:

The main manager bought some old cars with three wheels.

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Generalized Role Labeling using Propositional Representations

Current

Pred:

A1: The

A2:

A3:

Flags: @NEXT @1

Input Word: main | JJ_PRM1: *IZ-INM2:

The main manager bought some old cars with three wheels.

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Generalized Role Labeling using Propositional Representations

Current

Pred:

A1:

A2:

A3:

Flags:

Input Word: main| JJ_PRM1: PUT0M2:

The main manager bought some old cars with three wheels.

Top

Pred:

A1: The

A2:

A3:

Flags: @1

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Generalized Role Labeling using Propositional Representations

Current

Pred: main

A1:

A2:

A3:

Flags:

Input Word: main| JJ_PRM1: NEXTM2:

The main manager bought some old cars with three wheels.

Top

Pred:

A1: The

A2:

A3:

Flags: @1

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Generalized Role Labeling using Propositional Representations

Input Word: manager| DT_NM1: PUT1M2:

The main manager bought some old cars with three wheels.

Current

Pred: main

A1:

A2:

A3:

Flags: @NEXT

Top

Pred:

A1: The

A2:

A3:

Flags: @1

Page 35: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: manager| DT_NM1: OZ-OUTM2:

The main manager bought some old cars with three wheels.

Current

Pred: main

A1: manager

A2:

A3:

Flags: @NEXT

Top

Pred:

A1: The

A2:

A3:

Flags: @1

Page 36: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: manager| DT_NM1: PUT1M2:

The main manager bought some old cars with three wheels.

Current

Pred:

A1: The

A2:

A3:

Flags: @1 @OZ-OUT

P:main|A1:manager

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Generalized Role Labeling using Propositional Representations

Input Word: manager| DT_NM1: NEXTM2:

The main manager bought some old cars with three wheels.

Current

Pred:

A1: The manager

A2:

A3:

Flags: @1 @OZ-OUT

P:main|A1:manager

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Generalized Role Labeling using Propositional Representations

Input Word: bought| V_MAM1: PUT0M2:

The main manager bought some old cars with three wheels.

Current

Pred:

A1: The manager

A2:

A3:

Flags: @1 @NEXT

P:main|A1:manager

Page 39: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: bought| V_MAM1: NEXTM2:

The main manager bought some old cars with three wheels.

Current

Pred: bought

A1: The manager

A2:

A3:

Flags: @0

P:main|A1:manager

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Generalized Role Labeling using Propositional Representations

Input Word: some| DTM1: PUT2M2:

The main manager bought some old cars with three wheels.

Current

Pred: bought

A1: The manager

A2:

A3:

Flags: @0 @NEXT

P:main|A1:manager

Page 41: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: some| DTM1: NEXTM2:

The main manager bought some old cars with three wheels.

Current

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2

P:main|A1:manager

Page 42: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: old| JJ_PRM1: *IZ-INM2:

The main manager bought some old cars with three wheels.

Current

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2 @NEXT

P:main|A1:manager

Page 43: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: old| JJ_PRM1: PUT0M2:

The main manager bought some old cars with three wheels.

Current

Pred:

A1:

A2:

A3:

Flags: @IZ-IN

P:main|A1:manager

Top

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2

Page 44: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: old| JJ_PRM1: NEXTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred: old

A1:

A2:

A3:

Flags: @IZ-IN @0

Top

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2

Page 45: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: cars | DT_NM1: PUT1M2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred: old

A1:

A2:

A3:

Flags: @IZ-IN

@NEXT @0

Top

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2

Page 46: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: cars | DT_NM1: OZ-OUTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred: old

A1: cars

A2:

A3:

Flags: @IZ-IN

@NEXT @1

Top

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2

Page 47: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: cars | DT_NM1: PUT2M2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred: bought

A1: The manager

A2: some

A3:

Flags: @2

P:old|A1:cars

Page 48: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: cars | DT_NM1: NEXTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

P:old|A1:cars

Page 49: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: with | IIN_DTM1: IZ-IN1M2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2 @NEXT

P:old|A1:cars

Page 50: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: with | IIN_DTM1: PUT2M2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: cars

A2:

A3:

Flags: @1

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 51: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: with | IIN_DTM1: NEXTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: some cars

A2: with

A3:

Flags: @2

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 52: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: three | DTM1: PUT2M2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: some cars

A2: with

A3:

Flags: @2 @NEXT

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 53: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: three | DTM1: NEXTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: some cars

A2: with three

A3:

Flags: @2

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 54: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: wheels| DT_NM1: PUT2M2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: some cars

A2: with three

A3:

Flags: @2 @NEXT

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 55: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: wheels| DT_NM1: NEXTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: some cars

A2:with three wheels

A3:

Flags: @2

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 56: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Generalized Role Labeling using Propositional Representations

Input Word: .| DOTM1: OZ-OUTM2:

The main manager bought some old cars with three wheels.

P:main|A1:manager

Current

Pred:

A1: some cars

A2:with three wheels

A3:

Flags: @2 @NEXT

P:old|A1:cars

Top

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

Page 57: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Input Word: .| DOTM1: OZ-OUTM2:

P:main|A1:manager

Current

Pred: bought

A1: The manager

A2: some cars

A3:

Flags: @2

P:old|A1:cars

Generalized Role Labeling using Propositional Representations

The main manager bought some old cars with three wheels.

A1:cars|A2:with three wheels

Page 58: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Input Word: .| DOTM1: FINM2:

P:main|A1:manager

P:old|A1:cars

Generalized Role Labeling using Propositional Representations

The main manager bought some old cars with three wheels.

A1:cars|A2:with three wheels

P:bought|A1the managerA2:some cars

Page 59: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Training set

• SS-3-2- (DT_N I PUT1 NEXT)• SS-3-2- (V_MA took PUT0 NEXT)• SS-3-3- (DT_N her PUT2 NEXT)• SS-3-4- (IIN_DT to PUT3 NEXT)• SS-3-5- (DT_N school PUT3 NEXT)• SS-3-6- (. . OZ-OUT NEXT)• SS-3-7- (FIN)

Page 60: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company in 1999, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred: non-callable

A1: issue

A2:

A3:

Flags:

Page 61: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:can be put_back

A1:

A2: the issue

A3: to the company

Page 62: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:is priced

A1:

A2: the issue

A3: at 99 basis_points

Page 63: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:

A1: 99 basis_points

A2: above the

A3:

Page 64: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:

A1:

A2: treasury

A3:

Page 65: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:

A1: note

A2: 10-year

A3:

Page 66: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:

A1: note

A2: Treasury

A3:

Page 67: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

‘Elementary expressions’

• The non-callable issue, which can be put_back to the company, is priced at 99 basis_points above the Treasury 10-year note.

Current

Pred:is priced

A1:

A2: the issue

A3: at 99 basis_points

Page 68: Semantic Parsing based on Propositional Representations Fernandez, Sopena, Padró.

Training set• SS-17-1- (DT_N IMB PUT1 NEXT)• SS-17-2- (V_MA bought PUT0 NEXT)• SS-17-3- (DT the PUT2 NEXT)• SS-17-4- (DT_N team PUT2 NEXT)• SS-17-5- (IIN_DT from PUT3 NEXT)• SS-17-6- (DT_N BUMBRIGHT PUT3 NEXT)• SS-17-7- (IIN_DT for CONSTRUAL PUT2 NEXT)

• SS-17-8- (DT 145.000 PUT2 &BACK OZ-OUT PUT3 NEXT)

• SS-17-9- (DT_N $ PUT3 NEXT)• SS-17-10- (. . OZ-OUT NEXT)• SS-17-11- (FIN)

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FIN