The Role of Veridicality and Factivity in Clause Selection

The role of veridicality and factivity in clause selection

Aaron Steven White 1 Kyle Rawlins 2 NELS 48 University of Iceland 27th October, 2017

1University of Rochester, Department of Linguistics Institute for Data Science 2Johns Hopkins University, Department of Cognitive Science

1 Slides available at aswhite.net

2 Introduction Overarching question

Which of a verb’s semantic properties determine its syntactic distribution? Gruber 1965, Fillmore 1970, Zwicky 1971, Jackendoff 1972, Grimshaw 1979, 1990, Pesetsky 1982, 1991, Pinker 1989, Levin 1993

Semantic Syntactic Properties Distribution     + telic [ NP]     − durative [ S]   −  [ VP] stative   ......

4 Overarching question

Semantic Syntactic Properties Distribution     + telic [ NP]     − durative [ S]   −  [ VP] stative   ......

4 Overarching question

Semantic Syntactic Properties Distribution     + telic [ NP]     − durative [ S]   −  [ VP] stative   ......

4 Distribution of nominals Sensitive to event structural properties like stativity, telicity, du- rativity, causativity, transfer, etc. (see Levin & Rappaport Hovav 2005)

Distribution of clauses Sensitive to intentional properties like representationality, pref- erentiality, factivity/veridicality, communicativity, etc. Bolinger 1968, Hintikka 1975, Hooper 1975, Stalnaker 1984, Farkas 1985, Villalta 2000, 2008, Kratzer 2006, Egré 2008, Schefﬂer 2009, Moulton 2009, Anand & Hacquard 2013, Rawlins 2013, Portner & Rubinstein 2013, Anand & Hacquard 2014, Spector & Egré 2015, Bogal-Allbritten 2016, Theilier et al. 2017 among many others

An apparent split

5 Distribution of clauses Sensitive to intentional properties like representationality, pref- erentiality, factivity/veridicality, communicativity, etc. Bolinger 1968, Hintikka 1975, Hooper 1975, Stalnaker 1984, Farkas 1985, Villalta 2000, 2008, Kratzer 2006, Egré 2008, Schefﬂer 2009, Moulton 2009, Anand & Hacquard 2013, Rawlins 2013, Portner & Rubinstein 2013, Anand & Hacquard 2014, Spector & Egré 2015, Bogal-Allbritten 2016, Theilier et al. 2017 among many others

An apparent split

Distribution of nominals Sensitive to event structural properties like stativity, telicity, du- rativity, causativity, transfer, etc. (see Levin & Rappaport Hovav 2005)

5 An apparent split

Distribution of nominals Sensitive to event structural properties like stativity, telicity, du- rativity, causativity, transfer, etc. (see Levin & Rappaport Hovav 2005)

5 Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns (cf. Koenig & Davis 2001)

Not intentional properties (cf. White & Rawlins 2017)

Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content, resulting in a piece-wise semantic-to-syntax mapping

Overarching Hypothesis

6 Not intentional properties (cf. White & Rawlins 2017)

Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content, resulting in a piece-wise semantic-to-syntax mapping

Overarching Hypothesis

Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns (cf. Koenig & Davis 2001)

6 Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content, resulting in a piece-wise semantic-to-syntax mapping

Overarching Hypothesis

Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns (cf. Koenig & Davis 2001)

Not intentional properties (cf. White & Rawlins 2017)

6 Overarching Hypothesis

Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns (cf. Koenig & Davis 2001)

Not intentional properties (cf. White & Rawlins 2017)

Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content, resulting in a piece-wise semantic-to-syntax mapping

6 Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

1. ...selection doesn’t directly trafﬁc in these properties. 2. ...apparent correlations between selection and factivity and veridicality arise from... 2.1 ...only analyzing frequent verbs. 2.2 ...lack of attention to confounding event structural variables like transfer and stativity.

Today’s talk

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

7 1. ...selection doesn’t directly trafﬁc in these properties. 2. ...apparent correlations between selection and factivity and veridicality arise from... 2.1 ...only analyzing frequent verbs. 2.2 ...lack of attention to confounding event structural variables like transfer and stativity.

Today’s talk

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

7 2. ...apparent correlations between selection and factivity and veridicality arise from... 2.1 ...only analyzing frequent verbs. 2.2 ...lack of attention to confounding event structural variables like transfer and stativity.

Today’s talk

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

1. ...selection doesn’t directly trafﬁc in these properties.

7 2.1 ...only analyzing frequent verbs. 2.2 ...lack of attention to confounding event structural variables like transfer and stativity.

Today’s talk

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

1. ...selection doesn’t directly trafﬁc in these properties. 2. ...apparent correlations between selection and factivity and veridicality arise from...

7 2.2 ...lack of attention to confounding event structural variables like transfer and stativity.

Today’s talk

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

1. ...selection doesn’t directly trafﬁc in these properties. 2. ...apparent correlations between selection and factivity and veridicality arise from... 2.1 ...only analyzing frequent verbs.

7 Today’s talk

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Claims Use experiments that measure (i) syntactic distribution and (ii) factivity/veridicality for all clause-embedding verbs to show that...

Measuring syntactic distribution

Measuring veridicality and factivity

Results and analysis

Conclusion

Outline

Introduction

8 Measuring syntactic distribution

Measuring veridicality and factivity

Results and analysis

Conclusion

Outline

Introduction

Background: veridicality & factivity

8 Measuring veridicality and factivity

Results and analysis

Conclusion

Outline

Introduction

Background: veridicality & factivity

Measuring syntactic distribution

8 Results and analysis

Conclusion

Outline

Introduction

Background: veridicality & factivity

Measuring syntactic distribution

Measuring veridicality and factivity

8 Conclusion

Outline

Introduction

Background: veridicality & factivity

Measuring syntactic distribution

Measuring veridicality and factivity

Results and analysis

8 Outline

Introduction

Background: veridicality & factivity

Measuring syntactic distribution

Measuring veridicality and factivity

Results and analysis

Conclusion

8 Background: veridicality & factivity (1) a. Jo knew that Bo was alive → Bo was alive b. Jo proved that Bo was alive → Bo was alive

Factivity

A verb v is factive iff np v s presupposes s Kiparsky & Kiparsky 1970, Karttunen 1971b et seq

(2) a. Jo didn’t know that Bo was alive → Bo was alive b. Jo didn’t prove that Bo was alive ̸→ Bo was alive

Veridicality and factivity

Veridicality

A verb v is veridical iff np v s entails s Karttunen 1971a, Egré 2008, Kart- tunen 2012, Spector & Egré 2015 a.o.

10 b. Jo proved that Bo was alive → Bo was alive

Factivity

A verb v is factive iff np v s presupposes s Kiparsky & Kiparsky 1970, Karttunen 1971b et seq

(2) a. Jo didn’t know that Bo was alive → Bo was alive b. Jo didn’t prove that Bo was alive ̸→ Bo was alive

Veridicality and factivity

Veridicality

A verb v is veridical iff np v s entails s Karttunen 1971a, Egré 2008, Kart- tunen 2012, Spector & Egré 2015 a.o.

(1) a. Jo knew that Bo was alive → Bo was alive

10 Factivity

A verb v is factive iff np v s presupposes s Kiparsky & Kiparsky 1970, Karttunen 1971b et seq

(2) a. Jo didn’t know that Bo was alive → Bo was alive b. Jo didn’t prove that Bo was alive ̸→ Bo was alive

Veridicality and factivity

Veridicality

A verb v is veridical iff np v s entails s Karttunen 1971a, Egré 2008, Kart- tunen 2012, Spector & Egré 2015 a.o.

(1) a. Jo knew that Bo was alive → Bo was alive b. Jo proved that Bo was alive → Bo was alive

10 (2) a. Jo didn’t know that Bo was alive → Bo was alive b. Jo didn’t prove that Bo was alive ̸→ Bo was alive

Veridicality and factivity

Veridicality

A verb v is veridical iff np v s entails s Karttunen 1971a, Egré 2008, Kart- tunen 2012, Spector & Egré 2015 a.o.

(1) a. Jo knew that Bo was alive → Bo was alive b. Jo proved that Bo was alive → Bo was alive

Factivity

A verb v is factive iff np v s presupposes s Kiparsky & Kiparsky 1970, Karttunen 1971b et seq

10 b. Jo didn’t prove that Bo was alive ̸→ Bo was alive

Veridicality and factivity

Veridicality

A verb v is veridical iff np v s entails s Karttunen 1971a, Egré 2008, Kart- tunen 2012, Spector & Egré 2015 a.o.

(1) a. Jo knew that Bo was alive → Bo was alive b. Jo proved that Bo was alive → Bo was alive

Factivity

A verb v is factive iff np v s presupposes s Kiparsky & Kiparsky 1970, Karttunen 1971b et seq

(2) a. Jo didn’t know that Bo was alive → Bo was alive

10 Veridicality and factivity

Veridicality

A verb v is veridical iff np v s entails s Karttunen 1971a, Egré 2008, Kart- tunen 2012, Spector & Egré 2015 a.o.

(1) a. Jo knew that Bo was alive → Bo was alive b. Jo proved that Bo was alive → Bo was alive

Factivity

A verb v is factive iff np v s presupposes s Kiparsky & Kiparsky 1970, Karttunen 1971b et seq

(2) a. Jo didn’t know that Bo was alive → Bo was alive b. Jo didn’t prove that Bo was alive ̸→ Bo was alive 10 Veridicality/factivity and responsivity

Responsivity (Lahiri 2002) A verb is responsive iff it takes interrogatives and declaratives see also Karttunen 1977a,b, Groenendijk & Stokhof 1984 et seq

(3) a. Jo knew that Bo was alive. b. Jo knew whether Bo was alive.

Generalization

A verb is responsive iff {factive (Hintikka 1975) / veridical (Egré 2008)} see also George 2011, Uegaki 2012, 2015; cf. Beck & Rullmann 1999, Spector & Egré 2015

(4) a. Jo knew {that, whether} Bo was alive. b. Jo thought {that, *whether} Bo was alive. 11 Predicted correlation Responsivity

Factivity/Veridicality 12 Measuring syntactic distribution for 1000 clause-embedding verbs × 50 syntactic frames

Measuring syntactic distribution

MegaAttitude dataset (White & Rawlins 2016)

Ordinal (1-7 scale) acceptability ratings

14 × 50 syntactic frames

Measuring syntactic distribution

MegaAttitude dataset (White & Rawlins 2016)

Ordinal (1-7 scale) acceptability ratings for 1000 clause-embedding verbs

14 Measuring syntactic distribution

MegaAttitude dataset (White & Rawlins 2016)

Ordinal (1-7 scale) acceptability ratings for 1000 clause-embedding verbs × 50 syntactic frames

14 MegaAttitude verbs

15 Solution Construct semantically bleached frames using indeﬁnites

(5) a. know + NP _ed {that, whether} S Someone knew {that, whether} something happened. b. tell + NP _ed NP {that, whether} S Someone was told {that, whether} something happened. c. bother + NP was _ed {that, which NP} S Someone was bothered {that something, which thing} happened.

Sentence construction

Challenge Automate construction of a very large set of frames in a way that is sufﬁciently general to many verbs

16 (5) a. know + NP _ed {that, whether} S Someone knew {that, whether} something happened. b. tell + NP _ed NP {that, whether} S Someone was told {that, whether} something happened. c. bother + NP was _ed {that, which NP} S Someone was bothered {that something, which thing} happened.

Sentence construction

Challenge Automate construction of a very large set of frames in a way that is sufﬁciently general to many verbs

Solution Construct semantically bleached frames using indeﬁnites

16 b. tell + NP _ed NP {that, whether} S Someone was told {that, whether} something happened. c. bother + NP was _ed {that, which NP} S Someone was bothered {that something, which thing} happened.

Sentence construction

Challenge Automate construction of a very large set of frames in a way that is sufﬁciently general to many verbs

Solution Construct semantically bleached frames using indeﬁnites

(5) a. know + NP _ed {that, whether} S Someone knew {that, whether} something happened.

16 c. bother + NP was _ed {that, which NP} S Someone was bothered {that something, which thing} happened.

Sentence construction

Challenge Automate construction of a very large set of frames in a way that is sufﬁciently general to many verbs

Solution Construct semantically bleached frames using indeﬁnites

(5) a. know + NP _ed {that, whether} S Someone knew {that, whether} something happened. b. tell + NP _ed NP {that, whether} S Someone was told {that, whether} something happened.

16 Sentence construction

Challenge Automate construction of a very large set of frames in a way that is sufﬁciently general to many verbs

Solution Construct semantically bleached frames using indeﬁnites

16 Sentence construction

Challenge Automate construction of a very large set of frames in a way that is sufﬁciently general to many verbs

Solution Construct semantically bleached frames using indeﬁnites

16 Measuring veridicality and factivity Task

...you will be given a statement and a question related to that statement. Your task will be to respond yes, maybe or maybe not, or no to the question, assuming that the statement is true. (cf. Karttunen et al. 2014)

18 Task

19 Task

20 • 348 verbs only in the active frame • 142 only in the passive frame • 27 in both

1,088 items randomly partitioned into 16 lists of 68

Stimuli

517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

21 • 348 verbs only in the active frame • 142 only in the passive frame • 27 in both

1,088 items randomly partitioned into 16 lists of 68

Stimuli

517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

22 • 142 only in the passive frame • 27 in both

1,088 items randomly partitioned into 16 lists of 68

Stimuli

517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

• 348 verbs only in the active frame

22 • 27 in both

1,088 items randomly partitioned into 16 lists of 68

Stimuli

517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

• 348 verbs only in the active frame • 142 only in the passive frame

22 1,088 items randomly partitioned into 16 lists of 68

Stimuli

517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

• 348 verbs only in the active frame • 142 only in the passive frame • 27 in both

22 Stimuli

517 verbs from the MegaAttitude based on their acceptability in the [NP _ that S] and [NP was _ed that S] frames

• 348 verbs only in the active frame • 142 only in the passive frame • 27 in both

1,088 items randomly partitioned into 16 lists of 68

22 Passive

(7) a. Someone was told that a particular thing happened. b. Someone wasn’t told that a particular thing happened.

(8) a. Someone was bothered that a particular thing happened. b. Someone wasn’t bothered that a particular thing happened.

Stimuli

Active

(6) a. Someone thought that a particular thing happened. b. Someone didn’t think that a particular thing happened.

23 (8) a. Someone was bothered that a particular thing happened. b. Someone wasn’t bothered that a particular thing happened.

Stimuli

Active

(6) a. Someone thought that a particular thing happened. b. Someone didn’t think that a particular thing happened.

Passive

(7) a. Someone was told that a particular thing happened. b. Someone wasn’t told that a particular thing happened.

23 Stimuli

Active

(6) a. Someone thought that a particular thing happened. b. Someone didn’t think that a particular thing happened.

Passive

(7) a. Someone was told that a particular thing happened. b. Someone wasn’t told that a particular thing happened.

(8) a. Someone was bothered that a particular thing happened. b. Someone wasn’t bothered that a particular thing happened.

23 • 10 ratings per item... • ...given by 10 different participants

Participants

160 unique participants through Amazon’s Mechanical Turk

24 • ...given by 10 different participants

Participants

160 unique participants through Amazon’s Mechanical Turk

• 10 ratings per item...

24 Participants

160 unique participants through Amazon’s Mechanical Turk

• 10 ratings per item... • ...given by 10 different participants

24 Results and analysis Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 26 maybe maybe maybe Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 27 maybe maybe maybe Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 28 maybe maybe maybe Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 29 maybe maybe maybe Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 30 maybe maybe maybe Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 31 maybe maybe maybe Raw responses

know prove think V(p) ¬V(p)

no yes no yes no yes 32 maybe maybe maybe Transformation (roughly) Map each verb to single two-dimensional point by assigning -1 to no, 0 to maybe, and 1 to yes, then take the mean.

Normalize Use ridit scoring to normalize for how often a particular partic- ipant gives a particular response. (Similar to z-scoring.)

Normalization

33 Normalize Use ridit scoring to normalize for how often a particular partic- ipant gives a particular response. (Similar to z-scoring.)

Normalization

Transformation (roughly) Map each verb to single two-dimensional point by assigning -1 to no, 0 to maybe, and 1 to yes, then take the mean.

33 Normalized responses p → V(p) ← ¬p

¬p ← ¬V(p) → p 34 (Similar to z-scoring.)

Normalization

Transformation (roughly) Map each verb to single two-dimensional point by assigning -1 to no, 0 to maybe, and 1 to yes, then take the mean.

Normalize Use ridit scoring to normalize for how often a particular partic- ipant gives a particular response.

35 Normalized responses p → V(p) ← ¬p

¬p ← ¬V(p) → p 36 Normalized responses p → Nonveridicals decide promise think hope

V(p) wish

believe ← ¬p

Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 37 Normalized responses

Factives

know love surprise hate find_out p → Nonveridicals decide promise think hope

V(p) wish

believe ← ¬p

Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 38 Normalized responses

Veridicals Factives

verify show know love prove find surprise hate show find_out ensure

indicate p → Nonveridicals decide promise think hope

V(p) wish

believe ← ¬p

Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 39 Normalized responses

Veridicals Factives

verify show know love prove find surprise hate show find_out ensure

indicate p → Nonveridicals decide promise think hope

V(p) wish

believe ←

fabricate

¬p pretend Antiveridicals mislead

misinform fake Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 40 Normalized responses

Veridicals Factives

verify show know love prove find surprise hate show find_out ensure

indicate p → Nonveridicals decide promise think hope

V(p) wish

believe ←

fabricate hallucinate ¬p pretend Antiveridicals mislead Antifactives?

misinform fake Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 41 Normalized responses p → V(p) ← ¬p

¬p ← ¬V(p) → p 42 Normalized responses

Veridicals Factives

verify show know love prove find surprise hate show find_out ensure

indicate

Nonveridicals decide promise think hope wish

believe

fabricate Veridicality hallucinate pretend Antiveridicals mislead Antifactives?

misinform fake Frame a NP _ that S a NP was _ed that S

Factivity 43 Relating factivity, veridicality, and question-taking

Question Do factivity/veridicality positively correlate with question-taking?

44 Correlation: factivity and question-taking Acceptability of [_ CP[+Q]]

Factivity 45 Intuition If a verb is acceptable in some frame that contains an interrogative complement, it is acceptable with interrogatives.

Measure of question selection

Acceptability of [ CP[+Q]] For a particular verb, maximum acceptability over all frames that contain an interrogative complement.

46 Measure of question selection

Acceptability of [ CP[+Q]] For a particular verb, maximum acceptability over all frames that contain an interrogative complement.

Intuition If a verb is acceptable in some frame that contains an interrogative complement, it is acceptable with interrogatives.

46 Correlation: factivity and question-taking Acceptability of [_ CP[+Q]]

Factivity 47 Correlation: factivity and question-taking Acceptability of [_ CP[+Q]]

Factivity 48 Correlation: factivity and question-taking Acceptability of [_ CP[+Q]]

Factivity 49 Correlation: veridicality and question-taking Acceptability of [_ CP[+Q]]

Veridicality 50 Correlation: veridicality and question-taking Acceptability of [_ CP[+Q]]

Veridicality 51 Correlation: veridicality and question-taking Acceptability of [_ CP[+Q]]

Veridicality 52 Two hypotheses

1. Previous analyses were biased by verb frequency. 2. Our analysis missed subregularities due to verb class.

What’s going on?

Question How could we have gotten the direction of correlation so wrong?

53 2. Our analysis missed subregularities due to verb class.

What’s going on?

Question How could we have gotten the direction of correlation so wrong?

Two hypotheses

1. Previous analyses were biased by verb frequency.

53 Finding #1 If we look at only the most frequent verbs, the correlations ﬂip.

Finding #2 There are subregularities, but they don’t validate the purported correlation.

Two ﬁndings

54 Finding #2 There are subregularities, but they don’t validate the purported correlation.

Two ﬁndings

Finding #1 If we look at only the most frequent verbs, the correlations ﬂip.

54 Correlation: factivity with all verbs Acceptability of [_ CP[+Q]]

55 Factivity Correlation: factivity with high-frequency verbs

know

write think showshow tell see say find

require Acceptability of [_ CP[+Q]]

56 Factivity Correlation: veridicality with all verbs Acceptability of [_ CP[+Q]]

57 Veridicality Correlation: veridicality with high-frequency verbs

know

write tell think show show say see find

require Acceptability of [_ CP[+Q]]

58 Veridicality What’s going on?

Question How could we have gotten the direction of correlation so wrong?

Two hypotheses

1. Previous analyses were biased by verb frequency. 2. Our analysis missed subregularities due to verb class.

59 Finding #2 There are subregularities, but they don’t validate the purported correlation.

Two ﬁndings

Finding #1 If we look at only the most frequent verbs, the correlations ﬂip.

60 Two ﬁndings

Finding #1 If we look at only the most frequent verbs, the correlations ﬂip.

Finding #2 There are subregularities, but they don’t validate the purported correlation.

60 1. veridicality/factivity 2. full syntactic distribution (not just question-taking)

Possibility The question-taking correlation holds in some clusters.

Finding subregularities

Aim Find overlapping clusters of verbs that best explain both...

61 2. full syntactic distribution (not just question-taking)

Possibility The question-taking correlation holds in some clusters.

Finding subregularities

Aim Find overlapping clusters of verbs that best explain both...

1. veridicality/factivity

61 Possibility The question-taking correlation holds in some clusters.

Finding subregularities

Aim Find overlapping clusters of verbs that best explain both...

1. veridicality/factivity 2. full syntactic distribution (not just question-taking)

61 Finding subregularities

Aim Find overlapping clusters of verbs that best explain both...

1. veridicality/factivity 2. full syntactic distribution (not just question-taking)

Possibility The question-taking correlation holds in some clusters.

61 MegaAttitude frames

Syntactic type

NP PP S

ACTIVE PASSIVE COMP TENSE

that [+Q] for ∅ [+FIN] [-FIN] [ NP] [ NP PP] [ PP] [ PP S] [ NP S] [ S] whether which NP -ed would to ∅ -ing

62 1. veridicality/factivity to syntactic distribution 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

63 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

1. veridicality/factivity to syntactic distribution

63 Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

1. veridicality/factivity to syntactic distribution 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

63 Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

1. veridicality/factivity to syntactic distribution 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

63 Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

1. veridicality/factivity to syntactic distribution 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

63 Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

1. veridicality/factivity to syntactic distribution 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

63 Canonical Correlation Analysis (CCA)

Intuition Find best way of simultaneously mapping...

1. veridicality/factivity to syntactic distribution 2. syntactic distribution to veridicality/factivity

Veridicality Distributional representation representation

Veridicality Acceptability judgments judgments

63 CCA verb scores CCA Component 2

CCA Component 1 64 CCA verb scores CCA Component 2

CCA Component 1 65 CCA verb scores CCA Component 2

Class communicative emotive neither CCA Component 1 66 CCA verb scores CCA Component 2

Class communicative emotive neither CCA Component 1 67 CCA frame loadings

NP to NP

to NP that S

toto NP NP whether thatto NP S futurewhether S future S whichNP to VP

NP whichNP S S

about NP to NP thatthat S SnotenseS NP that S that S notense NP to VPstative whichNP to VP whichNPwhether S to VP NP VPing NP that S notense that S whichNP S null so whether S NP that S future whether S future that S notense that S future

that S future NP whether S future about NP so NP VP to VPeventive VPing CCA Component 2 Slift whetherabout S whether S

whether to VP null NP whether S NP to VPeventive whether S future about whether S Voice to VPeventiveto VPstative a active a passive for NPNP to to VP VPstative CCA Component 1 68 CCA feature loadings

npanim[T.to]

npinanim[T.bare]

complementizer[which]

complementizer[that]

complementizer[Q]tense[T.tensed]tense[T.past] tense[T.future] embedded_subject[T.TRUE]complementizer[direct] npinanim[T.about] complementizer[whether]complementizer[T]

progressive[T.TRUE]other[T.so] tense[T.null]npanim[T.bare] eventivity[T.eventive] other[T.about] infinitival[T.TRUE]complementizer[null] CCA Component 2 eventivity[T.stative] complementizer[aboutwhether]

complementizer[for] CCA Component 1 69 Positive ﬁnding Veridicality/factivity correlates with NP- and PP-taking (Goal / Experiencer arguments)

Discussion

Negative ﬁnding Veridicality/factivity does not correlate with question-taking

70 Discussion

Negative ﬁnding Veridicality/factivity does not correlate with question-taking

Positive ﬁnding Veridicality/factivity correlates with NP- and PP-taking (Goal / Experiencer arguments)

70 Possibility #2 Question selection can be reduced to semantic properties that control NP- and PP-taking

Discussion

Possibility #1 Veridicality/factivity can be reduced to semantic properties that control NP- and PP-taking.

71 Discussion

Possibility #1 Veridicality/factivity can be reduced to semantic properties that control NP- and PP-taking.

Possibility #2 Question selection can be reduced to semantic properties that control NP- and PP-taking

71 Conclusion Distribution of clauses Sensitive to intentional properties like representationality, pref- erentiality, factivity/veridicality, communicativity, etc.

Conclusion

Distribution of nominals Sensitive to event structural properties like stativity, telicity, du- rativity, causativity, transfer, etc.

73 Conclusion

Distribution of nominals Sensitive to event structural properties like stativity, telicity, du- rativity, causativity, transfer, etc.

Distribution of clauses Sensitive to intentional properties like representationality, pref- erentiality, factivity/veridicality, communicativity, etc.

73 Not intentional properties

Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content

Conclusion

Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns

74 Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content

Conclusion

Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns

Not intentional properties

74 Conclusion

Hypothesis The distribution of clauses is determined by the same semantic properties as the distribution of nouns

Not intentional properties

Intuition Intentional properties require that an eventuality have infor- mational content, but not all eventualities have such content

74 Findings

1. Veridicality and factivity do not correlate with question-taking 2. Veridicality and factivity correlate with NP- and PP-taking

Conclusion

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

75 2. Veridicality and factivity correlate with NP- and PP-taking

Conclusion

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Findings

1. Veridicality and factivity do not correlate with question-taking

75 Conclusion

Focus Two intentional properties—factivity and veridicality—that are argued to determine selection of interrogatives & declaratives

Findings

1. Veridicality and factivity do not correlate with question-taking 2. Veridicality and factivity correlate with NP- and PP-taking

75 Approach Attempt to explicitly measure semifactivity.

Future directions

Limitation We didn’t distinguish between factivity and semifactivity.

76 Future directions

Limitation We didn’t distinguish between factivity and semifactivity.

Approach Attempt to explicitly measure semifactivity.

76 Future directions

Old prompt Someone _ed that a particular thing happened. Did that thing happen?

New prompt If someone _ed that a particular thing happened, did that thing happen?

77 Measuring semifactivity

Veridicals Factives

verify show know love prove find surprise hate show find_out ensure

indicate p → Nonveridicals decide promise think hope

V(p) wish

believe ←

fabricate hallucinate ¬p pretend Antiveridicals mislead Antifactives?

misinform fake Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 78 Measuring semifactivity

verify show know love prove find surprise hate show find_out ensure

indicate p → decide

promise think hope

V(p) wish

believe ←

fabricate hallucinate ¬p pretend mislead

misinform fake Frame a NP _ that S a NP was _ed that S

¬p ← ¬V(p) → p 79 Thanks

We are grateful to audiences at Johns Hopkins University and the University of Rochester for discussion of this work. We would like to thank Rachel Rudinger and Ben Van Durme in particular for useful comments.

This work was partly funded by NSF INSPIRE BCS-1344269 (Gradient symbolic computation) and the JHU Science of Learning Institute.

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