the typology of veridicality inferences
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1 University of Rochester Department of Linguistics 2 Johns Hopkins University Department of Cognitive Science NELS 49 Cornell University 5 October 2018 The typology of veridicality inferences Aaron Steven White 1 Kyle Rawlins 2 0 Slides

  1. Limitation Because prior generalizations focus on finite interrogatives & But there is substantial variability in the veridicality inferences declaratives, prior dataset covered only finite complements. generated with different complements – even for the same verb. 5 Our prior work Question How direct is the relationship between content-dependent properties and syntactic distribution ? Focus Two content-dependent properties – factivity and veridicality – that are argued to determine selection of interrogatives & declaratives Prior finding (NELS 2017) But there are strong empirical reasons to believe they do not .

  2. But there is substantial variability in the veridicality inferences declaratives, prior dataset covered only finite complements. generated with different complements – even for the same verb. 5 Our prior work Question How direct is the relationship between content-dependent properties and syntactic distribution ? Focus Two content-dependent properties – factivity and veridicality – that are argued to determine selection of interrogatives & declaratives Prior finding (NELS 2017) But there are strong empirical reasons to believe they do not . Limitation Because prior generalizations focus on finite interrogatives &

  3. declaratives, prior dataset covered only finite complements. generated with different complements – even for the same verb. 5 Our prior work Question How direct is the relationship between content-dependent properties and syntactic distribution ? Focus Two content-dependent properties – factivity and veridicality – that are argued to determine selection of interrogatives & declaratives Prior finding (NELS 2017) But there are strong empirical reasons to believe they do not . Limitation Because prior generalizations focus on finite interrogatives & But there is substantial variability in the veridicality inferences

  4. Jo i knew that she i bought tofu. (3) a. Jo bought tofu. b. Jo forgot to buy tofu. Jo didn’t buy tofu. (4) a. Jo bought tofu. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu.

  5. Jo i knew that she i bought tofu. (3) a. b. Jo forgot to buy tofu. Jo didn’t buy tofu. (4) a. Jo bought tofu. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu.

  6. Jo i knew that she i bought tofu. (3) a. b. Jo forgot to buy tofu. Jo didn’t buy tofu. (4) a. Jo bought tofu. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu.

  7. Jo i knew that she i bought tofu. (3) a. b. (4) a. Jo bought tofu. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu. Jo forgot to buy tofu. → Jo didn’t buy tofu.

  8. (3) a. b. (4) a. Jo bought tofu. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu. Jo forgot to buy tofu. → Jo didn’t buy tofu. Jo i knew that she i bought tofu.

  9. (3) a. b. (4) a. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu. Jo forgot to buy tofu. → Jo didn’t buy tofu. Jo i knew that she i bought tofu. → Jo bought tofu.

  10. (3) a. b. (4) a. b. Jo knew to buy tofu. Jo {bought, didn’t buy} tofu. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu. Jo forgot to buy tofu. → Jo didn’t buy tofu. Jo i knew that she i bought tofu. → Jo bought tofu.

  11. (3) a. b. (4) a. b. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu. Jo forgot to buy tofu. → Jo didn’t buy tofu. Jo i knew that she i bought tofu. → Jo bought tofu. Jo knew to buy tofu. ̸→ Jo {bought, didn’t buy} tofu.

  12. (3) a. b. (4) a. b. 6 Variability in veridicality Jo i forgot that she i bought tofu. → Jo bought tofu. Jo forgot to buy tofu. → Jo didn’t buy tofu. Jo i knew that she i bought tofu. → Jo bought tofu. Jo knew to buy tofu. ̸→ Jo {bought, didn’t buy} tofu.

  13. Empirical contributions 1. Dataset capturing the variability of factivity and veridicality across finite and infinitival complement types . 2. Data-driven typology of inference patterns across comp. types. Analytical contributions 1. Inference pattern typology explains some parts of syntactic distribution reasonably well, but far from perfect. indirect , mediated by fine-grained verb class. Is there evidence that this variability correlates with distribution? 2. More likely that the veridicality-distribution relationship is 7 Today’s talk Question

  14. 2. Data-driven typology of inference patterns across comp. types. Analytical contributions 1. Inference pattern typology explains some parts of syntactic distribution reasonably well, but far from perfect. indirect , mediated by fine-grained verb class. Is there evidence that this variability correlates with distribution? 2. More likely that the veridicality-distribution relationship is 7 Today’s talk Question Empirical contributions 1. Dataset capturing the variability of factivity and veridicality across finite and infinitival complement types .

  15. Analytical contributions 1. Inference pattern typology explains some parts of syntactic distribution reasonably well, but far from perfect. indirect , mediated by fine-grained verb class. Is there evidence that this variability correlates with distribution? 2. More likely that the veridicality-distribution relationship is 7 Today’s talk Question Empirical contributions 1. Dataset capturing the variability of factivity and veridicality across finite and infinitival complement types . 2. Data-driven typology of inference patterns across comp. types.

  16. indirect , mediated by fine-grained verb class. 2. More likely that the veridicality-distribution relationship is Is there evidence that this variability correlates with distribution? 7 Today’s talk Question Empirical contributions 1. Dataset capturing the variability of factivity and veridicality across finite and infinitival complement types . 2. Data-driven typology of inference patterns across comp. types. Analytical contributions 1. Inference pattern typology explains some parts of syntactic distribution reasonably well, but far from perfect.

  17. 2. More likely that the veridicality-distribution relationship is Is there evidence that this variability correlates with distribution? 7 Today’s talk Question Empirical contributions 1. Dataset capturing the variability of factivity and veridicality across finite and infinitival complement types . 2. Data-driven typology of inference patterns across comp. types. Analytical contributions 1. Inference pattern typology explains some parts of syntactic distribution reasonably well, but far from perfect. indirect , mediated by fine-grained verb class.

  18. Introduction A new veridicality dataset Data overview Predicting distribution using veridicality Conclusion 8 Outline

  19. Introduction A new veridicality dataset Data overview Predicting distribution using veridicality Conclusion 8 Outline

  20. Introduction A new veridicality dataset Data overview Predicting distribution using veridicality Conclusion 8 Outline

  21. Introduction A new veridicality dataset Data overview Predicting distribution using veridicality Conclusion 8 Outline

  22. Introduction A new veridicality dataset Data overview Predicting distribution using veridicality Conclusion 8 Outline

  23. A new veridicality dataset

  24. MegaAcceptability dataset (White and Rawlins, 2016a) Ordinal (1-7 scale) acceptability ratings for 1000 clause-embedding verbs in 50 syntactic frames MegaVeridicality dataset (White and Rawlins, 2018) Veridicality judgments for 517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames wide variety of syntactic contexts. 9 Measuring veridicality and distribution Aim Measure syntactic distribution and veridicality inferences across a

  25. MegaVeridicality dataset (White and Rawlins, 2018) Veridicality judgments for 517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames wide variety of syntactic contexts. 9 Measuring veridicality and distribution Aim Measure syntactic distribution and veridicality inferences across a MegaAcceptability dataset (White and Rawlins, 2016a) Ordinal (1-7 scale) acceptability ratings for 1000 clause-embedding verbs in 50 syntactic frames

  26. 9 wide variety of syntactic contexts. Measuring veridicality and distribution Aim Measure syntactic distribution and veridicality inferences across a MegaAcceptability dataset (White and Rawlins, 2016a) Ordinal (1-7 scale) acceptability ratings for 1000 clause-embedding verbs in 50 syntactic frames MegaVeridicality dataset (White and Rawlins, 2018) Veridicality judgments for 517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

  27. 10 Veridicality judgment task

  28. 11 Veridicality judgment task

  29. • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 12 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements :

  30. • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) 2,850 items randomly partitioned into 50 lists of 57 12 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs)

  31. NP _ed NP to VP[+ev] NP _ed NP to VP[-ev] 13 Someone didn’t tell a particular person to do a particular thing. thing. Someone didn’t believe a particular person to have a particular b. Someone believed a particular person to have a particular thing. a. (7) Someone told a particular person to do a particular thing. b. a. (6) Someone didn’t want for a particular thing to happen. b. Someone wanted for a particular thing to happen. a. (5) Stimuli NP _ed for NP to VP

  32. • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) 2,850 items randomly partitioned into 50 lists of 57 14 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs)

  33. • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 14 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs)

  34. NP _ed NP to VP[-ev] 15 b. thing. Someone didn’t believe a particular person to have a particular b. Someone believed a particular person to have a particular thing. a. (7) Someone didn’t tell a particular person to do a particular thing. Someone told a particular person to do a particular thing. a. (6) Someone didn’t want for a particular thing to happen. b. Someone wanted for a particular thing to happen. a. (5) Stimuli NP _ed for NP to VP NP _ed NP to VP[+ev]

  35. • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 16 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs)

  36. • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 16 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs)

  37. 17 b. thing. Someone didn’t believe a particular person to have a particular b. Someone believed a particular person to have a particular thing. a. (7) Someone didn’t tell a particular person to do a particular thing. Someone told a particular person to do a particular thing. a. (6) Someone didn’t want for a particular thing to happen. b. Someone wanted for a particular thing to happen. a. (5) Stimuli NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[-ev]

  38. • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 18 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs)

  39. • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 18 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs)

  40. NP was _ed to VP[-ev] (8) a. A particular person was ordered to do a particular thing. b. A particular person wasn’t ordered to do a particular thing. (9) a. A particular person was overjoyed to have a particular thing. b. A particular person wasn’t overjoyed to have a particular thing. 19 Stimuli NP was _ed to VP[+ev]

  41. • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 20 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs)

  42. • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 20 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs)

  43. (8) a. A particular person was ordered to do a particular thing. b. A particular person wasn’t ordered to do a particular thing. (9) a. A particular person was overjoyed to have a particular thing. b. A particular person wasn’t overjoyed to have a particular thing. 21 Stimuli NP was _ed to VP[+ev] NP was _ed to VP[-ev]

  44. • [NP _ed to VP[+ev]] (217 verbs) • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 22 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs)

  45. • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 22 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs)

  46. NP _ed to VP[-ev] (10) a. A particular person decided to do a particular thing. b. A particular person didn’t decide to do a particular thing. (11) a. A particular person hoped to have a particular thing. b. A particular person didn’t hope to have a particular thing. 23 Stimuli NP _ed to VP[+ev]

  47. • [NP _ed to VP[-ev]] (165 verbs) 2,850 items randomly partitioned into 50 lists of 57 24 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs)

  48. 2,850 items randomly partitioned into 50 lists of 57 • [NP _ed to VP[-ev]] (165 verbs) 24 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs)

  49. (10) a. A particular person decided to do a particular thing. b. A particular person didn’t decide to do a particular thing. (11) a. A particular person hoped to have a particular thing. b. A particular person didn’t hope to have a particular thing. 25 Stimuli NP _ed to VP[+ev] NP _ed to VP[-ev]

  50. 2,850 items randomly partitioned into 50 lists of 57 • [NP _ed to VP[-ev]] (165 verbs) 26 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs)

  51. 2,850 items randomly partitioned into 50 lists of 57 • [NP _ed to VP[-ev]] (165 verbs) 26 Stimuli Expand MegaVeridicality with 603 verb types from MegaAcceptability based on acceptability in 7 frames involving infinitival complements : • [NP _ed for NP to VP] (184 verbs) • [NP _ed NP to VP[+ev]] (197 verbs) • [NP _ed NP to VP[-ev]] (128 verbs) • [NP was _ed NP to VP[+ev]] (278 verbs) • [NP was _ed NP to VP[-ev]] (256 verbs) • [NP _ed to VP[+ev]] (217 verbs)

  52. Intuition Mixed-effects ordinal model-based normalization to control for variability in how participants use the response scale. (see Agresti, 2014) Applied to both veridicality and acceptability judgments. Like z -scoring, but better models response behavior. 27 Results Note

  53. Intuition Mixed-effects ordinal model-based normalization to control for variability in how participants use the response scale. (see Agresti, 2014) Applied to both veridicality and acceptability judgments. Like z -scoring, but better models response behavior. 27 Results Note

  54. Mixed-effects ordinal model-based normalization to control for variability in how participants use the response scale. (see Agresti, 2014) Applied to both veridicality and acceptability judgments. Like z -scoring, but better models response behavior. 27 Results Note Intuition

  55. Data overview

  56. 28 Results that S for NP to VP NP to VP[+ev] know forget remember order bother want think hope tell remind order believe hope ¬p ← V(p) → p want pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother know remember remember know order bother want tell think want want believe hope hope pretend forget forget pretend ¬p ← ¬V(p) → p

  57. Example: y -axis A particular person didn’t forget to do a particular thing. A particular person forgot to do a particular thing. 29 Results Example: x -axis

  58. 30 Results that S for NP to VP NP to VP[+ev] know forget remember order bother want think hope tell remind order believe hope ¬p ← V(p) → p want pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother know remember remember know order bother want tell think want want believe hope hope pretend forget forget pretend ¬p ← ¬V(p) → p

  59. Example: y -axis A particular person didn’t forget to do a particular thing. A particular person forgot to do a particular thing. 31 Results Example: x -axis

  60. A particular person didn’t forget to do a particular thing. A particular person forgot to do a particular thing. 31 Results Example: x -axis Example: y -axis

  61. 32 Results that S for NP to VP NP to VP[+ev] know forget remember order bother want think hope tell remind order believe hope ¬p ← V(p) → p want pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother know remember remember know order bother want tell think want want believe hope hope pretend forget forget pretend ¬p ← ¬V(p) → p

  62. 33 Results that S for NP to VP NP to VP[+ev] know forget remember order bother want think hope tell remind order believe hope ¬p ← V(p) → p want pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother know remember remember know order bother want tell think want want believe hope hope pretend forget forget pretend ¬p ← ¬V(p) → p

  63. 34 Results that S for NP to VP NP to VP[+ev] know know forget forget remember remember order bother want think think hope hope tell remind order believe believe hope ¬p ← V(p) → p want pretend pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother know remember know order bother want remember tell think want want believe hope hope pretend forget forget pretend ¬p ← ¬V(p) → p

  64. 35 Results that S for NP to VP NP to VP[+ev] know know forget forget remember remember order bother want want think think hope hope tell remind order order believe believe hope hope ¬p ← V(p) → p want pretend pretend pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother know remember remember know order bother want tell think want want believe hope hope forget forget pretend pretend ¬p ← ¬V(p) → p

  65. 36 Results that S for NP to VP NP to VP[+ev] know know forget forget remember remember order bother want want think think hope hope tell remind order order believe believe hope hope ¬p ← V(p) → p want pretend pretend pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know bother bother know remember remember know know order bother want want remember tell think think want want believe hope hope hope forget pretend forget forget pretend pretend ¬p ← ¬V(p) → p

  66. 37 Results that S for NP to VP NP to VP[+ev] know know forget forget remember remember order order bother bother want want think think hope hope tell tell remind remind order order believe believe hope hope ¬p ← V(p) → p want want pretend pretend pretend pretend NP to VP[−ev] to VP[+ev] to VP[−ev] know know bother bother know know remember remember remember know know order order bother bother want want remember tell tell think think want want want want believe believe hope hope hope hope pretend forget forget forget forget pretend pretend pretend ¬p ← ¬V(p) → p

  67. Predicting distribution using veridicality

  68. Approach Method Extract patterns of inference – e.g. factive, veridical, or implicative. Use an automated method to discover inference patterns across verbs by decomposing veridical data into underlying factors. Regularized censored factor analysis with loss weighted by normalized acceptability and scores constrained to (-1, 1). Selected number of factors (12) using cross-validation procedure. (Ask about specifics after the talk.) 38 Preliminaries Goal

  69. Method Extract patterns of inference – e.g. factive, veridical, or implicative. Use an automated method to discover inference patterns across verbs by decomposing veridical data into underlying factors. Regularized censored factor analysis with loss weighted by normalized acceptability and scores constrained to (-1, 1). Selected number of factors (12) using cross-validation procedure. (Ask about specifics after the talk.) 38 Preliminaries Goal Approach

  70. Extract patterns of inference – e.g. factive, veridical, or implicative. Use an automated method to discover inference patterns across verbs by decomposing veridical data into underlying factors. Regularized censored factor analysis with loss weighted by normalized acceptability and scores constrained to (-1, 1). Selected number of factors (12) using cross-validation procedure. (Ask about specifics after the talk.) 38 Preliminaries Goal Approach Method

  71. Extract patterns of inference – e.g. factive, veridical, or implicative. Use an automated method to discover inference patterns across verbs by decomposing veridical data into underlying factors. Regularized censored factor analysis with loss weighted by normalized acceptability and scores constrained to (-1, 1). Selected number of factors (12) using cross-validation procedure. (Ask about specifics after the talk.) 38 Preliminaries Goal Approach Method

  72. Extract patterns of inference – e.g. factive, veridical, or implicative. Use an automated method to discover inference patterns across verbs by decomposing veridical data into underlying factors. Regularized censored factor analysis with loss weighted by normalized acceptability and scores constrained to (-1, 1). Selected number of factors (12) using cross-validation procedure. (Ask about specifics after the talk.) 38 Preliminaries Goal Approach Method

  73. 39 Inference patterns Pattern 0 Pattern 1 Pattern 2 Pattern 3 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 4 Pattern 5 Pattern 6 Pattern 7 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 8 Pattern 9 Pattern 10 Pattern 11 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Inference polarity Matrix polarity negative positive

  74. 40 Inference patterns Pattern 0 Pattern 1 Pattern 2 Pattern 3 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 4 Pattern 5 Pattern 6 Pattern 7 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 8 Pattern 9 Pattern 10 Pattern 11 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Inference polarity Matrix polarity negative positive

  75. 41 Inference patterns Pattern 0 Pattern 1 Pattern 2 Pattern 3 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 4 Pattern 5 Pattern 6 Pattern 7 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 8 Pattern 9 Pattern 10 Pattern 11 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Inference polarity Matrix polarity negative positive

  76. 42 Inference patterns ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 3

  77. 43 Inference patterns ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 3

  78. 44 Inference patterns: factivity/veridicality ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● realize ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● find out ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● know ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 3

  79. 45 Inference patterns: factivity/veridicality ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● realize ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● find out ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● know ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● prove ● ● ● ● ● ● ● ● ● ● ● ● verify ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 3

  80. 46 Inference patterns: factivity/veridicality ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● realize ● fake ● lie ● ● ● ● ● ● ● ● ● ● ● ● ● find out ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● know ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● prove ● ● ● ● ● ● ● ● ● ● ● ● verify ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pattern 3

  81. 47 Inference patterns Pattern 0 Pattern 1 Pattern 2 Pattern 3 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 4 Pattern 5 Pattern 6 Pattern 7 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Pattern 8 Pattern 9 Pattern 10 Pattern 11 NP _ed that S NP was _ed that S NP _ed for NP to VP NP _ed NP to VP[+ev] NP _ed NP to VP[−ev] NP _ed to VP[+ev] NP was _ed to VP[+ev] NP _ed to VP[−ev] NP was _ed to VP[−ev] Inference polarity Matrix polarity negative positive

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