1. Required vs. Optional
Arguments
The 2nd Annual Meeting of Statistical Translation And
GEneration using Semantics (STAGES)
Martha Palmer, James Martin,
Jinho D. Choi, Shumin Wu
University of Colorado at Boulder
January 6, 2011
Wednesday, January 12, 2011

2. 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) core arguments
• ARG1
: a coat (theme)
• ARG3
: at a discount rate (asset)
• ARG2
: from Alaska (source) adjuncts?
• ARG4
: for Mary (beneficiary)
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3. 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.
• ARG0
: John (agent)
• rel
: bought (buy.01)
• ARG1
: a coat (theme)
• ARGM-PRP
: for personal use (purpose or reason)
• ARGM-LOC: at Target (location)
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4. Numbered Arguments in PP
• Numbered arguments in preposition phrases (PP)
- Corpora: OntoNotes v4.0.
- Total # of verb predicates: 150,305
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 10 by: 2.14 with: 0.76 to: 7.63 from: 16.54 to: 55.85 into: 3.23
Most Freq. from: 0.06 for: 0.65 in: 4.29 for: 14.53 into: 5.58 in: 3.23
Prepositions with: 0.04 to: 0.65 on: 2.71 to: 5.79 in: 4.59 with: 3.23
(%) of: 0.03 on: 0.64 with: 2.66 with: 4.61 at: 2.57 toward: 3.23
about: 0.02 about: 0.56 from: 2.61 in: 3.14 for: 2.17
in: 0.01 in: 0.37 for: 2.28 as: 2.73 on: 2.02
to: 0.01 of: 0.32 as: 1.61 about: 2.20 below: 0.84
for: 0.01 at: 0.26 into: 1.43 on: 2.08 from: 0.84
b/w: 0.01 as: 0.19 of: 1.17 at: 2.04 as: 0.74
over: 0.01 from: 0.18 at: 1.04 into: 1.78 beyond: 0.64
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5. 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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6. Results from NPMI
• Collecting (VB, IN) pairs whose NPMI > 0.
• Top 10 (VB, IN) pairs.
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
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7. Results from NPMI
• NPMI scores measured for (“buy”, IN) pairs.
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.
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Wednesday, January 12, 2011

8. 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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Wednesday, January 12, 2011

9. Results from LPMI
• Selectional (VB, IN) pairs.
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
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10. Results from LPMI
• Top & bottom 5 additional (VB, IN) pairs.
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
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11. 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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12. 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.
S SQ SINV SBAR SBARQ
Active Simple Yes/no Inverted Subordinat Wh
Passive declarative question declarative ing clause question
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13. 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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14. Finding Required Arguments
• Sentence type distributions.
S SQ SINV SBAR SBARQ
Active 131,809 2,810 1,516 42 19
Passive 13,026 185 74 6 2
• Required arguments of “buy”.
A.S 0 1 2 3 4 REC PNC PRP
(406) 97.54 91.38 7.39 5.17 3.94 0.16 0.08 0.01
A.SQ 0 1 LOC CAU ADV DIS
(8) 100.00 75.00 0.11 0.11 0.04 0.01
P.S 1 0 3 2 TMP
(21) 95.24 28.57 19.05 4.76 0.05
ARG# in %, ARGM in NPMI
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15. Active Declarative Example
• Active S
- John bought himself a car for commuting so he doesn’t run
late.
• ARG0
: John (agent)
• rel
: bought (buy.01)
• 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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16. Active Question Example
• Active SQ
- Why/Where did John also buy this car yesterday?
• ARGM-CAU
: Why (cause)
• ARGM-LOC
: Where (location)
• ARG0
: John (agent)
• ARGM-ADV
: also (adverbial)
• rel
: buy
• ARG1
: this car (theme)
• ARGM-TMP
: yesterday (temporal)
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17. 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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