Putting meaning into your trees
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Putting Meaning Into Your Trees. Martha Palmer Paul Kingsbury, Olga Babko-Malaya, Scott Cotton, Nianwen Xue, Shijong Ryu, Ben Snyder PropBanks I and II site visit University of Pennsylvania, October 30, 2003. Powell met Zhu Rongji. battle. wrestle. join. debate.

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Putting Meaning Into Your Trees

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Putting meaning into your trees

Putting Meaning Into Your Trees

Martha Palmer

Paul Kingsbury, Olga Babko-Malaya, Scott Cotton,

Nianwen Xue, Shijong Ryu, Ben Snyder

PropBanks I and II site visit

University of Pennsylvania,

October 30, 2003


Proposition bank from sentences to propositions

Powell met Zhu Rongji

battle

wrestle

join

debate

Powell and Zhu Rongji met

consult

Powell met with Zhu Rongji

Proposition:meet(Powell, Zhu Rongji)

Powell and Zhu Rongji had a meeting

Proposition Bank:From Sentences to Propositions

meet(Somebody1, Somebody2)

. . .

When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane.

meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane))


Capturing semantic roles

Capturing semantic roles*

  • JK broke [ ARG1 the LCD Projector.]

  • [ARG1 The windows] were broken by the hurricane.

  • [ARG1 The vase] broke into pieces when it toppled over.

SUBJ

SUBJ

SUBJ

*See also Framenet, http://www.icsi.berkeley.edu/~framenet/


Outline

Outline

  • Introduction

  • Proposition Bank

    • Starting with Treebanks

    • Frames files

    • Annotation process and status

  • PropBank II

  • Automatic labelling of semantic roles

  • Chinese Proposition Bank


A treebanked sentence

  • (S (NP-SBJ Analysts)

  • (VP have

  • (VP been

  • (VP expecting

  • (NP (NP a GM-Jaguar pact)

  • (SBAR (WHNP-1that)

  • (S (NP-SBJ *T*-1)

  • (VP would

  • (VP give

  • (NP the U.S. car maker)

  • (NP (NP an eventual (ADJP 30 %) stake)

  • (PP-LOC in (NP the British company))))))))))))

VP

have

been

VP

expecting

SBAR

NP

a GM-Jaguar pact

WHNP-1

that

VP

give

NP

Analysts have been expecting a GM-Jaguar

pact that would give the U.S. car maker an

eventual 30% stake in the British company.

NP

the US car maker

NP

an eventual 30% stake

in

the British company

A TreeBanked Sentence

S

VP

NP-SBJ

Analysts

NP

S

VP

NP-SBJ

*T*-1

would

NP

PP-LOC


The same sentence propbanked

  • (S Arg0 (NP-SBJ Analysts)

  • (VP have

  • (VP been

  • (VP expecting

  • Arg1 (NP (NP a GM-Jaguar pact)

  • (SBAR (WHNP-1that)

  • (S Arg0 (NP-SBJ *T*-1)

  • (VP would

  • (VP give

  • Arg2 (NP the U.S. car maker)

  • Arg1 (NP (NP an eventual (ADJP 30 %) stake)

  • (PP-LOC in (NP the British company))))))))))))

a GM-Jaguar pact

Arg0

that would give

Arg1

*T*-1

an eventual 30% stake in the British company

Arg2

the US car maker

expect(Analysts, GM-J pact)

give(GM-J pact, US car maker, 30% stake)

The same sentence, PropBanked

have been expecting

Arg1

Arg0

Analysts


Putting meaning into your trees

Frames File Example: expect

Roles:

Arg0: expecter

Arg1: thing expected

Example: Transitive, active:

Portfolio managers expect further declines in

interest rates.

Arg0: Portfolio managers

REL: expect

Arg1: further declines in interestrates


Frames file example give

Frames File example: give

Roles:

Arg0: giver

Arg1: thing given

Arg2: entity given to

Example: double object

The executives gave the chefsa standing ovation.

Arg0: The executives

REL: gave

Arg2: the chefs

Arg1: a standing ovation


Trends in argument numbering

Trends in Argument Numbering

  • Arg0 = agent

  • Arg1 = direct object / theme / patient

  • Arg2 = indirect object / benefactive / instrument / attribute / end state

  • Arg3 = start point / benefactive / instrument / attribute

  • Arg4 = end point


Ergative unaccusative verbs

Ergative/Unaccusative Verbs

Roles (no ARG0 for unaccusative verbs)

Arg1 = Logical subject, patient, thing rising

Arg2 = EXT, amount risen

Arg3* = start point

Arg4 = end point

Sales rose 4% to $3.28 billion from $3.16 billion.

The Nasdaq composite index added 1.01

to 456.6 on paltry volume.


Function tags for english chinese arguments or adjuncts

Function tags for English/Chinese (arguments or adjuncts?)

  • Variety of ArgM’s (Arg#>4):

    • TMP - when?

    • LOC - where at?

    • DIR - where to?

    • MNR - how?

    • PRP -why?

    • TPC – topic

    • PRD -this argument refers to or modifies another

    • ADV –others

    • CND – conditional

    • DGR – degree

    • FRQ - frequency


Inflection

Inflection

  • Verbs also marked for tense/aspect

    • Passive/Active

    • Perfect/Progressive

    • Third singular (is has does was)

    • Present/Past/Future

    • Infinitives/Participles/Gerunds/Finites

  • Modals and negation marked as ArgMs


Word senses in propbank

Word Senses in PropBank

  • Orders to ignore word sense not feasible for 700+ verbs

    • Mary left the room

    • Mary left her daughter-in-law her pearls in her will

      Frameset leave.01 "move away from":

      Arg0: entity leaving

      Arg1: place left

      Frameset leave.02 "give":

      Arg0: giver

      Arg1: thing given

      Arg2: beneficiary

How do these relate to traditional word senses as in WordNet?


Overlap between groups and framesets 95

Overlap between Groups and Framesets – 95%

Frameset2

Frameset1

WN1 WN2 WN3 WN4

WN6 WN7 WN8 WN5 WN 9 WN10

WN11 WN12 WN13 WN 14

WN19 WN20

develop

Palmer, Dang & Fellbaum, NLE 2004


Annotator accuracy ita 84

Annotator accuracy – ITA 84%


English propbank status w paul kingsbury scott cotton

English PropBank Status - (w/ Paul Kingsbury & Scott Cotton)

  • Create Frame File for that verb - DONE

    • 3282 lemmas, 4400+ framesets

  • First pass: Automatic tagging (Joseph Rosenzweig)

  • Second pass: Double blind hand correction

    • 118K predicates – all but 300 done

  • Third pass: Solomonization (adjudication)

    • Betsy Klipple, Olga Babko-Malaya – 400 left

  • Frameset tags

    • 700+, double blind, almost adjudicated, 92% ITA

  • Quality Control and general cleanup


Quality control and general cleanup

Quality Control and General Cleanup

  • Frame File consistency checking

  • Coordination with NYU

    • Insuring compatibility of frames and format

  • Leftover tasks

    • have, be, become

    • Adjectival usages

  • General cleanup

    • Tense tagging

    • Finalizing treatment of split arguments, ex. say, and symmetric arguments, ex. match

    • Supplementing sparse data w/ Brown for selected verbs


Summary of english propbank paul kingsbury olga babko malaya scott cotton

Summary of English PropBankPaul Kingsbury, Olga Babko-Malaya, Scott Cotton


Propbank ii

PropBank II

  • Nominalizations NYU

  • Lexical Frames DONE

  • Event Variables, (including temporals and locatives)

  • More fine-grained sense tagging

    • Tagging nominalizations w/ WordNet sense

    • Selected verbs and nouns

  • Nominal Coreference

    • not names

  • Clausal Discourse connectives – selected subset


Propbank i

sense tags;

discourse connectives

{ }

help2,5 tax rate1

keep1

company1

PropBank I

I

Also, [Arg0substantially lower Dutch corporate tax rates] helped [Arg1[Arg0 the company] keep [Arg1 its tax outlay] [Arg3-PRD flat] [ArgM-ADV relative to earnings growth]].

Event variables;

nominal reference;

REL

Arg0

Arg1

Arg3-PRD

ArgM-ADV

help

tax rates

the company keep its tax outlay flat

keep

the company

its tax outlay

flat

relative to earnings…


Summary of multilingual treebanks propbanks

Summary of Multilingual TreeBanks, PropBanks

* Also 1M word English monolingual PropBank


Agenda

Agenda

  • PropBank I 10:30 – 10:50

    • Automatic labeling of semantic roles

    • Chinese Proposition Bank

  • Proposition Bank II 10:50 – 11:30

    • Event variables – Olga Babko Malaya

    • Sense tagging – Hoa Dang

    • Nominal coreference – Edward Loper

    • Discourse tagging – Aravind Joshi

  • Research Areas – 11:30 – 12:00

    • Moving forward – Mitch Marcus

    • Alignment improvement via dependency structures– Yuan Ding

    • Employing syntactic features in MT – Libin Shen

  • Lunch 12:00 – 1:30 White Dog

  • Research Area - 1:30 – 1:45

    • Clustering – Paul Kingsbury

  • DOD Program presentation – 1:45 – 2:15

  • Discussion 2:15 – 3:00


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