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

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

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*

  • 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

  • Introduction

  • Proposition Bank

    • Starting with Treebanks

    • Frames files

    • Annotation process and status

  • PropBank II

  • Automatic labelling of semantic roles

  • Chinese Proposition Bank


  • (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


  • (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


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

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

  • 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

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?)

  • 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

  • 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

  • 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%

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%


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

  • 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 PropBankPaul Kingsbury, Olga Babko-Malaya, Scott Cotton


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


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

* Also 1M word English monolingual PropBank


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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