CIS630 1

Penn

Putting Meaning Into Your Trees

Martha Palmer

Collaborators: Paul Kingsbury, Olga Babko-Malaya, Bert Xue, Scott Cotton Karin Kipper, Hoa Dang, Szuting Yi, Edward Loper, Jinying Chen, Tom Morton, William Schuler, Fei Xia, Joseph Rosenzweig, Dan Gildea, Christiane Fellbaum

September 8, 2003

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PennElusive nature of “meaning”

Natural Language Understanding

Natural Language Processing or Natural Language Engineering

Empirical techniques rule!

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PennStatistical Machine Translation results

CHINESE TEXT The japanese court before china photo

trade huge & lawsuit. A large amount of the proceedings before

the court dismissed workers. japan’s court, former chinese servant

industrial huge disasters lawsuit. Japanese Court Rejects Former Chinese

Slave Workers’ Lawsuit for Huge Compensation.

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PennLeverage from shallow techniques?

Still need an approximation of meaning for accurate MT, IR, Q&A, IESense taggingLabeled dependency structures

What do we have as available resources?

What can we do with them?

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PennOutline Introduction – need for semantics Sense tagging Issues highlighted by

Senseval1 VerbNet Senseval2 – groupings, impact on ITA Automatic WSD, impact on scores Proposition Bank

Framesets, automatic role labellers Hierarchy of sense distinctions

Mapping VerbNet to PropBank

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PennWordNet - Princeton On-line lexical reference (dictionary)

Words organized into synonym sets <=> concepts

Hypernyms (ISA), antonyms, meronyms (PART) Useful for checking selectional restrictions (doesn’t tell you what they should be)

Typical top nodes - 5 out of 25 (act, action, activity) (animal, fauna) (artifact) (attribute, property) (body, corpus)

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PennWordNet – president, 6 senses1. president -- (an executive officer of a firm or corporation) -->CORPORATE EXECUTIVE, BUSINESS EXECUTIVE… LEADER 2. President of the United States, President, Chief Executive -- (the person who

holds the office of head of state of the United States government; "the President likes to jog every morning")-->HEAD OF STATE, CHIEF OF STATE

3. president -- (the chief executive of a republic) -->HEAD OF STATE, CHIEF OF STATE

4. president, chairman, chairwoman, chair, chairperson -- (the officer who presides at the meetings of an organization; "address your remarks to the chairperson") --> PRESIDING OFFICER  LEADER

5. president -- (the head administrative officer of a college or university)-->  ACADEMIC ADMINISTRATOR  …. LEADER

6. President of the United States, President, Chief Executive -- (the office of the United States head of state; "a President is elected every four years")

--> PRESIDENCY, PRESIDENTSHIP POSITION

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PennLimitations to WordNet Poor inter-annotator agreement (73%)

Just sense tags - no representationsVery little mapping to syntaxNo predicate argument structure no selectional restrictions

No generalizations about sense distinctions

No hierarchical entries

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PennSIGLEX98/SENSEVAL Workshop on Word Sense Disambiguation

54 attendees, 24 systems, 3 languages 34 Words (Nouns, Verbs, Adjectives) Both supervised and unsupervised systems Training data, Test data

Hector senses - very corpus based (mapping to WordNet)

lexical samples - instances, not running text Inter-annotator agreement over 90%

ACL-SIGLEX98,SIGLEX99, CHUM00

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PennHector - bother, 10 senses 1. intransitive verb, - (make an effort), after negation,

usually with to infinitive; (of a person) to take the trouble or effort needed (to do something). Ex. “About 70 percent of the shareholders did not bother to vote at all.” 1.1 (can't be bothered), idiomatic, be unwilling to make the effort

needed (to do something), Ex. ``The calculations needed are so tedious that theorists cannot be bothered to do them.''

2. vi; after neg; with `about" or `with"; rarely cont – (of a person) to concern oneself (about something or

someone) “He did not bother about the noise of the typewriter because Danny could not hear it above the sound of the tractor.” 2.1 v-passive; with `about" or `with“ - (of a person) to be concerned

about or interested in (something) “The only thing I'm bothered about is the well-being of the club.”

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PennMismatches between lexicons:Hector - WordNet, shake

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PennLevin classes (3100 verbs)

47 top level classes, 193 second and third level

Based on pairs of syntactic frames. John broke the jar. / Jars break easily. / The jar broke.

John cut the bread. / Bread cuts easily. / *The bread cut. John hit the wall. / *Walls hit easily. / *The wall hit.

Reflect underlying semantic components contact, directed motion, exertion of force, change of state

Synonyms, syntactic patterns (conative), relations

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PennConfusions in Levin classes? Not semantically homogenous

{braid, clip, file, powder, pluck, etc...}

Multiple class listingshomonymy or polysemy?

Alternation contradictions?Carry verbs disallow the Conative, but include{push,pull,shove,kick,draw,yank,tug}also in Push/pull class, does take the Conative

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PennIntersective Levin classes

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PennRegular Sense Extensions

John pushed the chair. +force, +contact

John pushed the chairs apart. +ch-state

John pushed the chairs across the room. +ch-loc

John pushed at the chair. -ch-loc

The train whistled into the station. +ch-loc

The truck roared past the weigh station. +ch-loc

AMTA98,ACL98,TAG98

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PennIntersective Levin Classes

More syntactically and semantically coherentsets of syntactic patternsexplicit semantic componentsrelations between senses

  VERBNETwww.cis.upenn.edu/verbnet

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PennVerbNet

Computational verb lexicon

Clear association between syntax and semanticsSyntactic frames (LTAGs) and selectional restrictions

(WordNet)Lexical semantic information – predicate argument

structureSemantic components represented as predicatesLinks to WordNet senses

Entries based on refinement of Levin Classes

Inherent temporal properties represented explicitlyduring(E), end(E), result(E)

TAG00, AAAI00, Coling00

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PennVerbNet

Class entries: Verb classes allow us to capture generalizations about verb

behavior Verb classes are hierarchically organized Members have common semantic elements, thematic roles,

syntactic frames and coherent aspect

Verb entries: Each verb can refer to more than one class (for different senses) Each verb sense has a link to the appropriate synsets in WordNet

(but not all senses of WordNet may be covered) A verb may add more semantic information to the basic semantics

of its class

Basic Transitive A V P cause(Agent,E) /\

manner (during(E),directedmotion,Agent)/\

manner (end(E), forceful,Agent)/\

contact(end(E),Agent,Patient)

Conative AV at P manner (during (E), directedmotion, Agent)

¬contact(end(E),Agent,Patient)

With/against alternation A V I against/on P

cause(Agent, E) /\

manner(during (E),directedmotion, Instr)/\

manner(end(E), forceful, Instr)/\

contact (end(E), Instr, Patient)

MEMBERS: [bang(1,3),bash(1),... hit(2,4,7,10), kick (3),...]THEMATIC ROLES: Agent, Patient, InstrumentSELECT RESTRICTIONS: Agent(int_control), Patient(concrete),

Instrument(concrete)

FRAMES and PREDICATES:

Hit class – hit-18.1

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PennVERBNET

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PennVerbNet/WordNet

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PennMapping WN-Hector via VerbNet

SIGLEX99, LREC00

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PennSENSEVAL2 –ACL’01 Adam Kilgarriff, Phil Edmond and Martha Palmer

All-words task Lexical sample taskCzech BasqueDutch ChineseEnglish EnglishEstonian Italian

Japanese Korean Spanish Swedish

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PennEnglish Lexical Sample - Verbs

Preparation for Senseval 2manual tagging of 29 highly polysemous verbs

(call, draw, drift, carry, find, keep, turn,...)WordNet (pre-release version 1.7)

To handle unclear sense distinctionsdetect and eliminate redundant sensesdetect and cluster closely related senses

NOT ALLOWED

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PennWordNet – call, 28 senses1. name, call -- (assign a specified, proper name to; "They named their son David"; "The new school was named

after the famous Civil Rights leader") -> LABEL

2. call, telephone, call up, phone, ring -- (get or try to get into communication (with someone) by telephone;

"I tried to call you all night"; "Take two aspirin and call me in the morning")

->TELECOMMUNICATE

3. call -- (ascribe a quality to or give a name of a common noun that reflects a quality;

"He called me a bastard"; "She called her children lazy and ungrateful")

-> LABEL

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PennWordNet – call, 28 senses4. call, send for -- (order, request, or command to come; "She was called into the director's office"; "Call the police!")

-> ORDER

5. shout, shout out, cry, call, yell, scream, holler, hollo, squall -- (utter a sudden loud cry;

"she cried with pain when the doctor inserted the needle"; "I yelled to her from the window but she couldn't hear me")

-> UTTER

6. visit, call in, call -- (pay a brief visit; "The mayor likes to call on some of the prominent citizens")

-> MEET

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PennGroupings Methodology

Double blind groupings, adjudication Syntactic Criteria (VerbNet was useful)

Distinct subcategorization frames call him a bastard call him a taxi

Recognizable alternations – regular sense extensions: play an instrument play a song play a melody on an instrument

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PennGroupings Methodology (cont.)

Semantic Criteria Differences in semantic classes of arguments

Abstract/concrete, human/animal, animate/inanimate, different instrument types,…

Differences in the number and type of arguments Often reflected in subcategorization frames John left the room. I left my pearls to my daughter-in-law in my will.

Differences in entailments Change of prior entity or creation of a new entity?

Differences in types of events Abstract/concrete/mental/emotional/….

Specialized subject domains

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PennWordNet: - call, 28 senses

WN2 , WN13,WN28 WN15 WN26

WN3 WN19 WN4 WN 7 WN8 WN9

WN1 WN22

WN20 WN25

WN18 WN27

WN5 WN 16 WN6 WN23

WN12

WN17 , WN 11 WN10, WN14, WN21, WN24

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PennWordNet: - call, 28 senses, groups

WN2, WN13,WN28 WN15 WN26

WN3 WN19 WN4 WN 7 WN8 WN9

WN1 WN22

WN20 WN25

WN18 WN27

WN5 WN 16 WN6 WN23

WN12

WN17 , WN 11 WN10, WN14, WN21, WN24,

Phone/radio

Label

Loud cry

Bird or animal cry

Request

Call a loan/bond

Visit

Challenge

Bid

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PennWordNet – call, 28 senses, Group11. name, call -- (assign a specified, proper name to; "They named their son David"; "The new school was named

after the famous Civil Rights leader") --> LABEL3. call -- (ascribe a quality to or give a name of a common

noun that reflects a quality; "He called me a bastard"; "She called her children lazy and

ungrateful") --> LABEL

19. call -- (consider or regard as being; "I would not call her beautiful")--> SEE

22. address, call -- (greet, as with a prescribed form, title, or name;

"He always addresses me with `Sir'"; "Call me Mister"; "She calls him by first name")

--> ADDRESS

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PennSense Groups: verb ‘develop’

WN1 WN2 WN3 WN4

WN6 WN7 WN8 WN5 WN 9 WN10

WN11 WN12 WN13 WN 14

WN19 WN20

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PennResults – averaged over 28 verbs

Call Develop Total

WN/corpus 28/14 21/16 16.28/10.83

Grp/corp 11/7 9/6 8.07/5.90

Entropy 3.68 3.17 2.81

ITA-fine 69% 67% 71%

ITA-coarse 89% 85% 82%

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PennMaximum Entropy WSDHoa Dang (in progress)

Maximum entropy frameworkcombines different features with no assumption of

independenceestimates conditional probability that W has sense X in

context Y, (where Y is a conjunction of linguistic features

feature weights are determined from training dataweights produce a maximum entropy probability

distribution

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PennFeatures used Topical contextual linguistic feature for W:

presence of automatically determined keywords in S Local contextual linguistic features for W:

presence of subject, complementswords in subject, complement positions, particles, prepsnoun synonyms and hypernyms for subjects,

complementsnamed entity tag (PERSON, LOCATION,..) for proper

Nswords within +/- 2 word window

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PennGrouping improved sense identification for MxWSD

75% with training and testing on grouped senses vs. 43% with training and testing on fine-grained senses Most commonly confused senses suggest grouping:

(1) name, call--assign a specified proper name to; ``They called their son David'' (2) call--ascribe a quality to or give a name that reflects a quality; ``He called me a bastard''; (3) call--consider or regard as being; ``I would not call her beautiful'' (4) address, call--greet, as with a prescribed form, title, or name; ``Call me Mister''; ``She calls him by his first name''

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PennResults – averaged over 28 verbs

Total

WN/corpus 16.28/10.83

Grp/corp 8.07/5.90

Entropy 2.81

ITA-fine 71%

ITA-coarse 82%

MX-fine 59%

MX-coarse 69%

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PennResults - first 5 Senseval2 verbs

Verb Begin

Call Carry

Develop

Draw Dress

WN/corpus

10/9 28/14 39/22 21/16 35/21 15/8

Grp/corp 10/9 11/7 16/11 9/6 15/9 7/4

Entropy 1.76 3.68 3.97 3.17 4.60 2.89

ITA-fine .812 .693 .607 .678 .767 .865

ITA-coarse

.814 .892 .753 .852 .825 1.00

MX-fine .832 .470 .379 .493 .366 .610

MX-coarse

.832 .636 .485 .681 .512 .898

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PennSummary of WSD

Choice of features is more important than choice of machine learning algorithm

Importance of syntactic structure (English WSD but not Chinese) Importance of dependencies Importance of an hierarchical approach to

sense distinctions, and quick adaptation to new usages.

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PennOutline Introduction – need for semantics Sense tagging Issues highlighted by

Senseval1 VerbNet Senseval2 – groupings, impact on ITA Automatic WSD, impact on scores Proposition Bank

Framesets, automatic role labellers Hierarchy of sense distinctions

Mapping VerbNet to PropBank

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PennProposition Bank:From Sentences to Propositions

Powell met Zhu Rongji

Proposition: meet(Powell, Zhu Rongji)Powell met with Zhu Rongji

Powell and Zhu Rongji met

Powell and Zhu Rongji had a meeting

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

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

debate

consult

joinwrestle

battle

meet(Somebody1, Somebody2)

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PennCapturing semantic roles*

Charles 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/

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PennA TreeBanked Sentence

Analysts

S

NP-SBJ

VP

have VP

been VP

expectingNP

a GM-Jaguar pact

NP

that

SBAR

WHNP-1

*T*-1

S

NP-SBJVP

wouldVP

give

the US car maker

NP

NP

an eventual 30% stake

NP

the British company

NP

PP-LOC

in

(S (NP-SBJ Analysts) (VP have (VP been (VP expecting

(NP (NP a GM-Jaguar pact) (SBAR (WHNP-1 that)

(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))))))))))))

Analysts have been expecting a GM-Jaguar pact that would give the U.S. car maker an eventual 30% stake in the British company.

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PennThe same sentence, PropBanked

Analysts

have been expecting

a GM-Jaguar pact

Arg0 Arg1

(S Arg0 (NP-SBJ Analysts) (VP have (VP been (VP expecting

Arg1 (NP (NP a GM-Jaguar pact) (SBAR (WHNP-1 that)

(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))))))))))))

that would give

*T*-1

the US car maker

an eventual 30% stake in the British company

Arg0

Arg2

Arg1

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

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Penn

English PropBankhttp://www.cis.upenn.edu/~ace/ 1M words of Treebank over 2 years,

May’01-03 New semantic augmentations

Predicate-argument relations for verbslabel arguments: Arg0, Arg1, Arg2, …First subtask, 300K word financial subcorpus (12K sentences, 29K predicates,1700 lemmas)

Spin-off: Guidelines FRAMES FILES - (necessary for annotators)3500+ verbs with labeled examples, rich

semantics, 118K predicates

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PennFrames Example: expectRoles: 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 interest rates

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PennFrames File example: giveRoles: Arg0: giver Arg1: thing given Arg2: entity given to

Example: double object The executives gave the chefs a standing

ovation. Arg0: The executives REL: gave Arg2: the chefs Arg1: a standing ovation

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PennHow are arguments numbered?

Examination of example sentences Determination of required / highly preferred

elements Sequential numbering, Arg0 is typical first

argument, except ergative/unaccusative verbs (shake example) Arguments mapped for "synonymous" verbs

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PennTrends 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

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Penn

Additional tags (arguments or adjuncts?)

Variety of ArgM’s (Arg#>4): TMP - when?

LOC - where at?

DIR - where to?

MNR - how?

PRP -why?

REC - himself, themselves, each other

PRD -this argument refers to or modifies another

ADV -others

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PennInflection 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

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PennPhrasal Verbs Put together Put in Put off Put on Put out Put up ...

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PennErgative/Unaccusative Verbs: rise

RolesArg1 = 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.

*Note: Have to mention prep explicitly, Arg3-from, Arg4-to, or could haveused ArgM-Source, ArgM-Goal. Arbitrary distinction.

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PennSynonymous Verbs: add in sense riseRoles:

Arg1 = Logical subject, patient, thing rising/gaining/being added to

Arg2 = EXT, amount risen

Arg4 = end point

The Nasdaq composite index added 1.01 to 456.6 on paltry volume.

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PennAnnotation procedure

Extraction of all sentences with given verb First pass: Automatic tagging (Joseph

Rosenzweig) http://www.cis.upenn.edu/~josephr/TIDES/index.html#lexicon

Second pass: Double blind hand correction Variety of backgrounds Less syntactic training than for treebanking

Tagging tool highlights discrepancies Third pass: Solomonization (adjudication)

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PennInter-Annotator Agreement

0

10

20

30

40

50

60

70

80

90

100

Pe

rce

nta

ge

Ag

ree

me

nt

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PennSolomonizationAlso , substantially lower Dutch corporate tax rates helped

the company keep its tax outlay flat relative to earnings growth.

*** Kate said:arg0 : the companyarg1 : its tax outlayarg3-PRD : flatargM-MNR : relative to earnings growth*** Katherine said:arg0 : the companyarg1 : its tax outlayarg3-PRD : flatargM-ADV : relative to earnings growth

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Penn

Automatic Labelling of Semantic Relations

Features: Predicate Phrase Type Parse Tree Path Position (Before/after predicate) Voice (active/passive) Head Word

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PennLabelling Accuracy-Known Boundaries

79.673.682.0Automatic

83.177.0Gold Standard

PropBank > 10 instances

PropBankFramenet

Parses

Accuracy of semantic role prediction for known boundaries--the system is given the constituents to classify.Framenet examples (training/test) are handpicked to be unambiguous.

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Penn

Labelling Accuracy – Unknown Boundaries

57.7 50.064.6 61.2Automatic

71.1 64.4Gold Standard

PropBank

Precision Recall

Framenet

Precision Recall

Parses

Accuracy of semantic role prediction for unknown boundaries--the system must identify the constituents as arguments and give them the correct roles.

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PennAdditional Automatic Role Labelers

Szuting Yi – EM clustering, unsupervisedConditional Random Fields

Yinying Chen - using role labels as features for WSD, decision trees, supervised, EM clustering, unsupervised

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PennOutline Introduction – need for semantics Sense tagging Issues highlighted by Senseval1 VerbNet Senseval2 – groupings, impact on ITA Automatic WSD, impact on scores Proposition Bank

Framesets, automatic role labellers Hierarchy of sense distinctions

Mapping VerbNet to PropBank

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PennFrames: Multiple Framesets Framesets are not necessarily consistent between

different senses of the same verb Verb with multiple senses can have multiple frames, but

not necessarily Roles and mappings onto argument labels are

consistent between different verbs that share similar argument structures, Similar to Framenet

Levin / VerbNet classes http://www.cis.upenn.edu/~dgildea/VerbNet

Out of the 720 most frequent verbs: 1 frameset 470 2 framesets 155 3+ framesets 95 (includes light verbs)

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PennWord 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 leavingArg1: place left

Frameset leave.02 "give":Arg0: giver Arg1: thing givenArg2: beneficiary

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

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PennWordNet: - leave, 14 senses

WN1 WN5 WN3 WN7

WN8

WN2 WN12 WN9 WN10 WN13

WN14

WN4

WN6 WN11

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PennWordNet: - leave, groups

WN1 WN5 WN3 WN7

WN8

WN2 WN12 WN9 WN10 WN13

WN14

WN4

WN6 WN11

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PennWordNet: - leave, framesets

WN1 WN5 WN3 WN7

WN8

WN2 WN12 WN9 WN10 WN13

WN14

WN4

WN6 WN11

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PennOverlap between Groups and Framesets – 95%

WN1 WN2 WN3 WN4

WN6 WN7 WN8 WN5 WN 9 WN10

WN11 WN12 WN13 WN 14

WN19 WN20

Frameset1

Frameset2

develop

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PennSense Hierarchy Framesets – coarse grained distinctions

Sense Groups (Senseval-2) intermediate level (includes Levin classes) – 95% overlap

WordNet – fine grained distinctions

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Pennleave.01 - move away from VerbNet Levin class: escape-51.1-1; WordNet Senses: WN 1, 5, 8

Thematic Roles: Location[+concrete] Theme[+concrete]

Frames with Semantics Basic Intransitive

"The convict escaped" motion(during(E),Theme) direction(during(E),Prep,Theme,?Location)

Intransitive (+ path PP) "The convict escaped from the prison"

Locative Preposition Drop "The convict escaped the prison"

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Pennleave.02 - give VerbNet Levin class: future_having-13.3 ; WordNet Senses: WN 2,10,13

Thematic Roles: Agent[+animate OR +organization] Recipient[+animate OR +organization] Theme[]

Frames with Semantics Dative

"I promised somebody my time" Agent V Recipient Theme has_possession(start(E),Agent,Theme) future_possession(end(E),Recipient,Theme) cause(Agent,E)

Transitive (+ Recipient PP) "We offered our paycheck to her" Agent V Theme Prep(to) Recipient )

Transitive (Theme Object) "I promised my house (to somebody)" Agent V Theme

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Penn

Propbank to VN mapping from Text meaning workshop Cluster verbs based on frames of arg labels

K-nearest neighbors EM

Compare derived clusters to VerbNet classes

sim(X, Y) =

Only a rough measure Not all verbs in VerbNet are attested in PropBank Not all verbs in PropBank are treated in VerbNet

YXYX

PropBank Frame for Clustering

For [Arg4

Mr. Sherwin], [Arg0

a conviction] could

[Rel

carry] [Arg1

penalties of five years in prison and

a $250,000 fine on each count](wsj_1331)

reduces to:

arg4 arg0 rel arg1

• Frameset tags, ~7K annotations, 200 schemas, 921 verbs

1, tran, 2 – ditran, 3 unaccusative

3 10 20 30 40 50 60 70 80 90 100 110 120 130 140 1500.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

Average Similarity

Number of Clusters

Sim

ilarit

y

Adding to VerbNet Classes 36.3 'combative meetings'

fight, consult, ... Clustering analysis adds hedge

Hedge one's bets against ... But some investors might prefer a simpler strategy

than hedging their individual holdings (wsj\_1962) Thus, buying puts after a big market slide can be an

expensive way to hedge against risk (wsj\_2415)

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PennLexical Semantics at Penn

Annotation of Penn Treebank with semantic role labels (propositions) and sense tags

Links to VerbNet and WordNetProvides additional semantic information that

clearly distinguishes verb sensesClass based to facilitate extension to previously

unseen usages

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PennPropBank 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]].

relative to earnings…

flatits tax outlaythe companykeep

the company keep its tax outlay flat

tax rateshelp

ArgM-ADVArg3-PRD

Arg1Arg0REL

Event variables;

ID#h23

k16

nominal reference;sense tags;

help2,5 tax rate1

keep1 company1

discourse connectives

{ }

I