Required vs. Optional Arguments

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Required vs. Optional Arguments

  1. 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, 2011Wednesday, January 12, 2011
  2. 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) 2Wednesday, January 12, 2011
  3. 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) 3Wednesday, January 12, 2011
  4. 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 4Wednesday, January 12, 2011
  5. 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. 5Wednesday, January 12, 2011
  6. 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 6Wednesday, January 12, 2011
  7. 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. 7Wednesday, January 12, 2011
  8. 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. 8Wednesday, January 12, 2011
  9. 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 9Wednesday, January 12, 2011
  10. 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 10Wednesday, January 12, 2011
  11. 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 11Wednesday, January 12, 2011
  12. 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 12Wednesday, January 12, 2011
  13. 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. 13Wednesday, January 12, 2011
  14. 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 14Wednesday, January 12, 2011
  15. 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) 15Wednesday, January 12, 2011
  16. 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) 16Wednesday, January 12, 2011
  17. 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. 17Wednesday, January 12, 2011

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