Required vs. Optional Arguments

Required vs. OptionalArguments

Martha Palmer, James Martin, Jinho D. Choi, Shumin Wu

University of Colorado at BoulderJanuary 6, 2011

The 2nd Annual Meeting of Statistical Translation And GEneration using Semantics (STAGES)

Wednesday, January 12, 2011

Arguments in PropBank• Numbered arguments (ARG#)

- Arguments that frequently co-occur with their predicates.

- They can be either core arguments or adjuncts.

- John bought a coat at a discount rate from Alaska for Mary.

• ARG0 : John (agent)

• rel : bought (buy.01)

• ARG1 : a coat (theme)

• ARG3 : at a discount rate (asset)

• ARG2 : from Alaska (source)

• ARG4 : for Mary (beneficiary)

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core arguments

adjuncts?


Arguments in PropBank• Modifiers (ARGM-TAG)

- Adjuncts annotated with their semantic roles.

- We also tag negations (M-NEG) and modals (M-MOD).

- John bought a coat for personal use at Target.



• ARG1 : a coat (theme)

• ARGM-PRP : for personal use (purpose or reason)

• ARGM-LOC: at Target (location)

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Numbered Arguments in PP• Numbered arguments in preposition phrases (PP)

- Corpora: OntoNotes v4.0.

- Total # of verb predicates: 150,305

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ARG0 ARG1 ARG2 ARG3 ARG4 ARG5

Total (#) 94,523 138,710 44,885 2,642 2,025 31

PP (%) 2.37% 5.18% 32.08% 61.05% 79.70% 12.90%

Top 10Most Freq.Prepositions(%)

by: 2.14from: 0.06with: 0.04of: 0.03about: 0.02in: 0.01to: 0.01for: 0.01b/w: 0.01over: 0.01

with: 0.76for: 0.65to: 0.65on: 0.64about: 0.56in: 0.37of: 0.32at: 0.26as: 0.19from: 0.18

to: 7.63in: 4.29on: 2.71with: 2.66from: 2.61for: 2.28as: 1.61into: 1.43of: 1.17at: 1.04

from: 16.54for: 14.53to: 5.79with: 4.61in: 3.14as: 2.73about: 2.20on: 2.08at: 2.04into: 1.78

to: 55.85into: 5.58in: 4.59at: 2.57for: 2.17on: 2.02below: 0.84from: 0.84as: 0.74beyond: 0.64

into: 3.23in: 3.23with: 3.23toward: 3.23


Required vs. Optional PPs• Distinguishing required PPs from optional PPs.

- Find meaningful (VB, IN ∈ A) pairs (A = a set of arguments).

- Pointwise Mutual Information (PMI)

- (Jointly) Normalized PMI

- Frequency cutoff : 1 < #(VB, IN ∈ A)

- Do not count “by” in passive constructions.

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Results from NPMI• Collecting (VB, IN) pairs whose NPMI > 0.

• Top 10 (VB, IN) pairs.

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Lexicon #(VB, IN) #(IN) #(VB) NPMI

campaign_alongside 2 4 17 0.6520

post_@ 8 8 124 0.6323

tilt_toward 3 59 5 0.6310

talk_about 342 1,077 564 0.6275

log_onto 4 36 8 0.6066

lie_notwithstanding 2 2 92 0.6053

commit_agaisnt 2 2 93 0.6009

sandwich_between 2 113 2 0.5936

revolve_around 3 70 6 0.5863

scatter_throughout 3 28 18 0.5795


Results from NPMI• NPMI scores measured for (“buy”, IN) pairs.

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Lexicon #(VB, IN) #(IN) #(VB) NPMI

buy_from 27 2,389 423 0.1336

buy_at 24 2,965 423 0.0975

buy_during 3 370 423 0.0806

buy_for 29 4,517 423 0.0751

buy_under 2 336 423 0.0524

buy_as 6 1,643 423 0.0135

buy_in 30 12,619 423 -0.0293

buy_into 2 1,002 423 -0.0373

buy_on 7 3,924 423 -0.0518

buy_with 5 3,743 423 -0.0759

We found 3,881 (VB, IN) pairs.


Required vs. Optional PPs• Numbered arguments against modifiers.

- ARG# are generally more important than ARGM.

- Given VB, find IN more likely to be ARG# than ARGM.

- Collecting (VB, IN) pairs whose LPMI > 0.

- There are 1,453 (VB, IN) pairs.

- We found 90 additional (VB, IN) pairs that were not found by NPMI.

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Results from LPMI• Selectional (VB, IN) pairs.

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Rank Lexicon P(IN∈ARG#|VB) P(IN∈ARGM|VB) LPMI

1 tamper_with 0.6364 ∈ 13.3635

2 depend_on 0.5226 ∈ 13.1665

3 allude_to 0.5000 ∈ 13.1224

...............

1,128 come_to 0.1088 0.0026 3.7458

1,129 turn_into 0.0942 0.0024 3.6797

1,130 move_to 0.1530 0.0039 3.6579

1,131 bring_to 0.0996 0.0027 3.6202

...............

2,516 be_across ∈ 0.0004 -6.0166

2,517 be_throughout ∈ 0.0005 -6.1985

2,518 be_besides ∈ 0.0007 -6.4856


Results from LPMI• Top & bottom 5 additional (VB, IN) pairs.

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Rank Lexicon P(IN∈ARG#|VB) P(IN∈ARGM|VB) LPMI

10 bang_on 0.5000 ∈ 13.1224

14 mingle_with 0.5000 ∈ 13.1224

417 channel_into 0.1429 ∈ 11.8696

811 feature_in 0.0317 ∈ 10.3656

860 belong_in 0.0240 ∈ 10.0859

...............

1,379 establish_for 0.0081 0.0066 0.2067

1,408 work_about 0.0017 0.0014 0.1604

1,424 stop_with 0.0052 0.0047 0.1097

1,440 reach_to 0.0086 0.0081 0.0635

1,450 know_to 0.0030 0.0029 0.0171


Finding Required Arguments• Syntax vs. semantic

- Using syntax, semantic, or both to find required arguments?

• Syntax : SBJ, OBJ, PP, etc.

• Semantic : ARG#, ARGM-TAG

- John bought a coat at a discount rate from Alaska for Mary.

• Syntax : SBJ, OBJ, [PP at], [PP from], [PP for]

• Semantic : ARG0, ARG1, ARG3, ARG2, ARG4

- John bought a coat for personal use at Target.

• Syntax : SBJ, OBJ, [PP for], [PP at]

• Semantic : ARG0, ARG1, ARGM-PRP, ARGM-LOC

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Finding Required Arguments• Finding required PropBank arguments.

- Different constructions require different sets of arguments.

• Active vs. passive constructions.

• Declarative vs. comment vs. question vs. ...

- Different verb senses may require different sets of args.

• Experiments

- Find required arguments for 10 different groups.

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S SQ SINV SBAR SBARQActive Simple

declarativeYes/no

questionInverted

declarativeSubordinating clause

WhquestionPassive

Simpledeclarative

Yes/noquestion

Inverteddeclarative

Subordinating clause

Whquestion


Finding Required Arguments• Finding required numbered arguments

- Preserve ones that P(ARG#|VB) > threshold.

- Count empty categories.

• Finding required modifiers.

- Preserve ones that NPMI(VB; ARGM) > 0.

- Ignore ARGM-NEG and ARGM-MOD.

• These experiments can be much more fine-grained, if we use verb senses instead of verb predicates.

- Future work.

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Finding Required Arguments• Sentence type distributions.

• Required arguments of “buy”.

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S SQ SINV SBAR SBARQActive 131,809 2,810 1,516 42 19Passive 13,026 185 74 6 2

A.S(406)

0 1 2 3 4 REC PNC PRPA.S(406) 97.54 91.38 7.39 5.17 3.94 0.16 0.08 0.01A.SQ(8)

0 1 LOC CAU ADV DISA.SQ(8) 100.00 75.00 0.11 0.11 0.04 0.01P.S

(21)1 0 3 2 TMPP.S

(21) 95.24 28.57 19.05 4.76 0.05

ARG# in %, ARGM in NPMI


Active Declarative Example• Active S

- John bought himself a car for commuting so he doesn’t run late.



• ARGM-REC : himself (reciprocal)

• ARG1 : a car (theme)

• ARGM-PNC : for commuting (purpose)

• ARGM-PRP : so he doesn’t run late (purpose or reason)

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Active Question Example• Active SQ

- Why/Where did John also buy this car yesterday?

• ARGM-CAU : Why (cause)

• ARGM-LOC : Where (location)


• ARGM-ADV : also (adverbial)

• rel : buy

• ARG1 : this car (theme)

• ARGM-TMP : yesterday (temporal)

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Future Work• Find required argument combinations.

- e.g., [ARG0] buy [ARG1] [ARG2] [ARG4] [ARGM-PRP]

- Use the predicate-argument structure to find transitivity.

• Use VerbNet, Tree-adjoining grammar:

- To find required arguments.

- To find transitivity.

• Find required arguments by verb senses.

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Required vs. Optional Arguments

Real Estate

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