the typology of veridicality inferences
play

The typology of veridicality inferences Aaron Steven White 1 Kyle - PowerPoint PPT Presentation

Sep 11, 2022 •292 likes •1.83k views

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

Recommend

Chapter 8 Slide 1 Inferences from Two Samples 8-1 Overview 8-2 Inferences about Two Proportions

Chapter 8 Slide 1 Inferences from Two Samples 8-1 Overview 8-2 Inferences about Two Proportions

Chapter 8 Slide 1 Inferences from Two Samples 8-1 Overview 8-2 Inferences about Two Proportions 8-3 Inferences about Two Means: Independent Samples 8-4 Inferences about Matched Pairs 8-5 Comparing Variation in Two Samples Chapter 8, Triola,

760 views • 61 slides
Canonical Typology Danny Hieber Hieber, Daniel W. 2011. Canonical Typology. Talk given to the

Canonical Typology Danny Hieber Hieber, Daniel W. 2011. Canonical Typology. Talk given to the

Canonical Typology Danny Hieber Hieber, Daniel W. 2011. Canonical Typology. Talk given to the Content Development 1 Department, Rosetta Stone, Harrisonburg, VA, Sept 2011. Outline 1. Overview of Typology 2. Overview of Canonical Typology 3.

647 views • 31 slides
The role of veridicality and factivity in clause selection Aaron Steven White 1 Kyle Rawlins 2 27

The role of veridicality and factivity in clause selection Aaron Steven White 1 Kyle Rawlins 2 27

NELS 48 University of Iceland 1 University of Rochester, Department of Linguistics Institute for Data Science 2 Johns Hopkins University, Department of Cognitive Science 1 The role of veridicality and factivity in clause selection Aaron Steven

1.6k views • 149 slides
A Holistic and Sustainable Care Center Project Typology - His istory ry & & Trends

A Holistic and Sustainable Care Center Project Typology - His istory ry & & Trends

A Holistic and Sustainable Care Center Project Typology - His istory ry & & Trends Tatiana Acosta | Thayse Darzi | Elisabet Lyon | Esperanza Muino 1 TYPOLOGY HIS ISTORY 2 TRENDS 3 TABLE OF CONTENTS GUID IDELINES Typology

407 views • 28 slides
LEXICAL TYPOLOGY Peter Koch (Part I) Koch, Lexical typology, 2010-8-24 A. General introduction

LEXICAL TYPOLOGY Peter Koch (Part I) Koch, Lexical typology, 2010-8-24 A. General introduction

LEXICAL TYPOLOGY Peter Koch (Part I) Koch, Lexical typology, 2010-8-24 A. General introduction B. Lexical hierarchies C. Lexical motivation D. Syntagmatic axis E. Outlook Koch, Lexical typology, 2010-8-24 1. The problem of the tertium

847 views • 69 slides
LEXICAL TYPOLOGY LEXICAL TYPOLOGY Peter Koch (Part III) Department of Romance Studies, Tbingen

LEXICAL TYPOLOGY LEXICAL TYPOLOGY Peter Koch (Part III) Department of Romance Studies, Tbingen

LEXICAL TYPOLOGY LEXICAL TYPOLOGY Peter Koch (Part III) Department of Romance Studies, Tbingen University peter.koch@uni-tuebingen.de http://homepages.unituebingen.de/peter.koch/index.htm Koch, Lexical typology, 2010827 1 10.

791 views • 43 slides
LEXICAL TYPOLOGY LEXICAL TYPOLOGY Peter Koch (Part II) Department of Romance Studies, Tbingen

LEXICAL TYPOLOGY LEXICAL TYPOLOGY Peter Koch (Part II) Department of Romance Studies, Tbingen

LEXICAL TYPOLOGY LEXICAL TYPOLOGY Peter Koch (Part II) Department of Romance Studies, Tbingen University peter.koch@uni-tuebingen.de http://homepages.unituebingen.de/peter.koch/index.htm Koch, Lexical typology, 2010825 1 6. Lexical

764 views • 54 slides
Exploring the typology of quantity-insensitive stress systems without gradient constraints Jeff

Exploring the typology of quantity-insensitive stress systems without gradient constraints Jeff

Exploring the typology of quantity-insensitive stress systems without gradient constraints Jeff Heinz, Greg Kobele, and Jason Riggle LSA Annual Meeting Oakland, California January 7, 2005 1 Metrical typology and quadratic constraints

294 views • 8 slides
Unit 1: Introduction to data Ultimate goal: make inferences about populations 1. Data

Unit 1: Introduction to data Ultimate goal: make inferences about populations 1. Data

1. Use a sample to make inferences about the population Unit 1: Introduction to data Ultimate goal: make inferences about populations 1. Data Collection + Caveat: populations are difficult or impossible to access Observational studies

319 views • 3 slides
Modal inferences in marked indefinites Maria Aloni [joint work with Angelika Port] [Special

Modal inferences in marked indefinites Maria Aloni [joint work with Angelika Port] [Special

Modal inferences in marked indefinites Maria Aloni [joint work with Angelika Port] [Special thanks to Floris Roelofsen and Michael Franke] University of Amsterdam, ILLC MIT linguistics colloquium 18 November 2011 Modal inferences in

820 views • 47 slides
Language Typology and Areal Linguistics Yiru July 13, 2016 Yiru Language Typology July 13,

Language Typology and Areal Linguistics Yiru July 13, 2016 Yiru Language Typology July 13,

Language Typology and Areal Linguistics Yiru July 13, 2016 Yiru Language Typology July 13, 2016 1 / 26 Overview Introduction 1 Typologically-based Clusters 2 Areal Linguistics 3 Yiru Language Typology July 13, 2016 2 / 26 Language

884 views • 26 slides
Formal Concept Analysis Kow Kuroda meets grammar typology Medical School, Kyorin

Formal Concept Analysis Kow Kuroda meets grammar typology Medical School, Kyorin

At the 21st Annual Meeting of the Association for Natural Language Processing (March 17, 2015, Kyoto Univerty, Japan) Formal Concept Analysis Kow Kuroda meets grammar typology Medical School, Kyorin University FCA meets grammar typology

567 views • 42 slides
Chapter 2 1 2.1: Inferences about 1 Test of interest throughout regression: Need sampling

Chapter 2 1 2.1: Inferences about 1 Test of interest throughout regression: Need sampling

Chapter 2 1 2.1: Inferences about 1 Test of interest throughout regression: Need sampling distribution of the estimator b 1 . Idea: If b 1 can be written as a linear combination of the responses (which are independent and normally

996 views • 73 slides
Should Security Researchers Experiment More and Draw More Inferences? * * With thanks to Walter

Should Security Researchers Experiment More and Draw More Inferences? * * With thanks to Walter

Should Security Researchers Experiment More and Draw More Inferences? * * With thanks to Walter Tichys Should Computer Scientists Experiment More? (1998) Kevin Killourhy with Roy Maxion Carnegie Mellon University CSET 2011 (August

402 views • 22 slides
On the proper use of phylogenetic information in typology Gerhard Jger Tbingen University

On the proper use of phylogenetic information in typology Gerhard Jger Tbingen University

On the proper use of phylogenetic information in typology Gerhard Jger Tbingen University Workshop Phylogenetic Linguistics and Linguistic Theory York, November 15, 2018 Gerhard Jger (Tbingen) Phylogenetic typology LanGeLin Workshop 1

637 views • 45 slides
CPSC 121: Models of Computation Module 7: Predicate Logic and Inferences Module 7: Predicate

CPSC 121: Models of Computation Module 7: Predicate Logic and Inferences Module 7: Predicate

CPSC 121: Models of Computation Module 7: Predicate Logic and Inferences Module 7: Predicate Logic and Inferences By the start of class, you should be able to: Determine the negation of any quantified statement. Given a quantified statement and

696 views • 28 slides
From Incremental to Transformational : A Three-Tiered Typology of Sustainable Development

From Incremental to Transformational : A Three-Tiered Typology of Sustainable Development

From Incremental to Transformational : A Three-Tiered Typology of Sustainable Development Performance Indicators #Reporting4Sustainability Geneva | 4 June 2019 Bill Baue Senior Director Reporting 3.0 @bbaue #Reporting3 Sustainability

527 views • 7 slides
Transcription, transliteration, transduction, and translation A typology of crosslinguistic name

Transcription, transliteration, transduction, and translation A typology of crosslinguistic name

Transcription, transliteration, transduction, and translation A typology of crosslinguistic name representation strategies Deryle Lonsdale BYU Linguistics lonz@byu.edu FHTW 2005 1 The crossroads Many NLP applications treat personal

615 views • 27 slides
Checking Normality One of the standard assumptions that ensure that inferences are valid is that

Checking Normality One of the standard assumptions that ensure that inferences are valid is that

ST 430/514 Introduction to Regression Analysis/Statistics for Management and the Social Sciences II Checking Normality One of the standard assumptions that ensure that inferences are valid is that the random errors = Y E ( Y | x ) are

618 views • 26 slides
Development of a Development of a Rural Typology GI S for Rural Typology GI S for Policy Makers

Development of a Development of a Rural Typology GI S for Rural Typology GI S for Policy Makers

Development of a Development of a Rural Typology GI S for Rural Typology GI S for Policy Makers Policy Makers Michael Shambaugh- -Miller, Ph.D. Miller, Ph.D. Michael Shambaugh RUPRI Center for Rural Health Policy Analysis RUPRI Center for

804 views • 42 slides
VOI: Value of Information (or Very Opaque Inferences?) Pete Naylor SPE Evening Meeting, 27

VOI: Value of Information (or Very Opaque Inferences?) Pete Naylor SPE Evening Meeting, 27

VOI: Value of Information (or Very Opaque Inferences?) Pete Naylor SPE Evening Meeting, 27 November 2012 Legal notice Certain statements included in this presentation contain forward-looking information concerning BG Group plcs strategy,

471 views • 36 slides
Tools for comparing and choosing between alternative phylogenetic inferences Steven Woolley,

Tools for comparing and choosing between alternative phylogenetic inferences Steven Woolley,

Tools for comparing and choosing between alternative phylogenetic inferences Steven Woolley, Samuel Harrington and Alan Templeton Washington University in St. Louis, Missouri Outline Motivation Current Progress Future

381 views • 13 slides
Refining the Motivational Approach to Symmetry-to-(un)reality Inferences Case Study:

Refining the Motivational Approach to Symmetry-to-(un)reality Inferences Case Study:

Refining the Motivational Approach to Symmetry-to-(un)reality Inferences Case Study: Aharonov-Bohm Effect Niels Martens Foundations2018, Utrecht 12 July 2018 A1. Virtual particles B1. Computer simulations A2. Naturalness B2. Model building

361 views • 17 slides
I Have a Narrow Thought Process: Constraints on Explanations Connecting Inferences and

I Have a Narrow Thought Process: Constraints on Explanations Connecting Inferences and

I Have a Narrow Thought Process: Constraints on Explanations Connecting Inferences and Self-Perceptions Emilee Rader, Samantha Hautea, and Anjali Munasinghe emilee@msu.edu, hauteasa@msu.edu, amunasinghe1620@gmail.com Department of Media

370 views • 11 slides

More recommend