Vacuous and Non-Vacuous Behaviors of the Present Tense

Transcription

1 Vacuous and Non-Vacuous Behaviors of the Present Tense Laine Stranahan Department of Linguistics, Harvard University 88 th Annual Meeting of the Linguistic Society of America January 2, 2014

2 Outline 1. Phenomenon 2. Background 2.1 Tense 2.2 Lifetime Inference 2.3 Presupposition Maximization 3. Analysis 3.1 Distributivity 3.2 Strongest Meaning Hypothesis 3.3 Individuals vs. Intervals 4. Open Issues

3 The Phenomenon Individuals # JFK and Joe Kennedy are tall. # My uncles are blond. # John and Bill are both very handsome. (Mittwoch, 2008) Intervals 2012 and 2016 are leap years. The even-numbered days this week are school days. Every Tuesday this month I fast. (Sauerland, 2002)

4 Semantic Types Individuals: e Truth Values: t (Worlds: w) Intervals: i/t

5 Tense as Presuppositional (Partee, 1973) PRESENT = λt : t t 0.t PAST = λt : t < t 0.t John e T i asleep < i, < e, t >> PRESENT < i, i > t i

6 Tense as Presuppositional (Partee, 1973) PRESENT = λt : t t 0.t PAST = λt : t < t 0.t t John e T i asleep < i, < e, t >> PRESENT < i, i > t i

7 The Present Tense is Vacuous (Sauerland, 2002) Every Tuesday this month I fast. (Sauerland, 2002) 2012 and 2016 are leap years. The even-numbered days this week are school days. PRESENT = λt.t PAST = λt : t < t 0.t

8 The Present Tense is Vacuous (Sauerland,2002) John is asleep t Asleep(John,t)

9 The Present Tense is Vacuous (Sauerland,2002) John is asleep t Asleep(John,t) <PRESENT, PAST > t < t 0 Maxim: Make your contribution presuppose as much as possible.

10 The Present Tense is Vacuous (Sauerland,2002) John is asleep t Asleep(John,t) <PRESENT, PAST > t < t 0 Maxim: Make your contribution presuppose as much as possible. John is asleep = t Alive(John,t) (t < t 0 ) Antipresupposition

11 Non-Vacuous Behavior # JFK and Joe Kennedy are tall. # My uncles are blond. # John and Bill are both very handsome. (Mittwoch, 2008)

12 I-Level and S-Level Predicates (Carlson, 1977; Chierchia, 1995; Kratzer, 1995) I-Level tall blond handsome. S-Level asleep available drunk.

13 I-Level and S-Level Predicates I-Level Properties P: x[ t 1 [P(x, t 1 )] t 2 [Alive(x, t 2 ) P(x, t 2 )]] S-Level Properties P: x[ t 1 [P(x, t 1 )] t 2 [Alive(x, t 2 ) P(x, t 2 )]]

14 Lifetime Inference (Musan, 1997) John was blond. John is no longer alive.

15 Lifetime Inference (Musan, 1997) John was blond. John is no longer alive. 1. Speaker utters John was blond. 2. John is blond would have been more informative/relevant. 3. Speaker is trying to be maximally informative. 4. Speaker must not believe John is still blond. 5. By the semantics of blond, if John were alive, he would be blond. 6. John must not be alive.

16 Inferences About Lifetimes Past Present I-Level John was blond. Lifetime Inference John is no longer alive. John is blond. John is alive. S-Level John was asleep. John was alive. John is asleep. John is alive.

17 Inferences About Lifetimes blond = λt.λx.x is blond at t and x is alive at t asleep = λt.λx.x is asleep at t and x is alive at t

18 Individual Lifetime Parameter John is dead. John = j t t < t 0 Bill is alive. Bill = b t t t 0

19 Individual Lifetime Parameter John is dead. John = j t t < t 0 Bill is alive. Bill = b t t t 0 Mary was a WWII nurse. Mary = m t t

20 Individual Lifetime Parameter John is dead. John = j t t < t 0 Bill is alive. Bill = b t t t 0 Mary was a WWII nurse. Mary = m t t Fred is asleep. Fred = f t t t 0 Sam was blonde. Sam = s t t < t 0

21 Maximize Presupposition (Heim, 1991; Percus, 2006; Sauerland, 2008; Singh, 2011; Schlenker, 2012) The/#a sun was shining. (Hawkins, 1991) Both/#all of John s eyes are blue. (Percus, 2006) John knows/#thinks that Paris is in France. (Singh, 2011)

22 Maximize Presupposition (Heim, 1991; Percus, 2006; Sauerland, 2008; Singh, 2011; Schlenker, 2012) The/#a sun was shining. (Hawkins, 1991) Both/#all of John s eyes are blue. (Percus, 2006) John knows/#thinks that Paris is in France. (Singh, 2011) Maxim: Make your contribution presuppose as much as possible.

23 Local MP (Percus, 2006; Singh, 2011) Everyone with exactly two students assigned the same exercise to both/#all of his students. If John has exactly two students and he assigned the same exercise to both/#all of them, then I m sure he ll be happy. Mary believes that John has exactly two students and that he assigned the same exercise to both/#all of them.

24 Local MP (Percus, 2006; Singh, 2011) Everyone with exactly two students assigned the same exercise to both/#all of his students. If John has exactly two students and he assigned the same exercise to both/#all of them, then I m sure he ll be happy. Mary believes that John has exactly two students and that he assigned the same exercise to both/#all of them. Maximize ( Presupposition )(MP): [S... X...] MP ALT = P([ S... X...]) [ S... X...] P([ S... X...]) Y ALT (X)[ P([ S... Y...])], where: i. ALT(X) = {Y: [ S...Y...] presupposes more than [ S...X...]} ii. P(S) = presupposition(s) of S

25 John is asleep. PRESENT = λt.t, PAST = λt.t t < t 0 ALT (PRESENT) = {PAST} 1. MP {PAST} ([ Asleep ( PRESENT (t))]( John )) 2. MP {PAST} ( t[[[λt.λx.x is asleep at t x is alive at t]([λt.t](t))](j)]) ( ) [[λt.λx.x is asleep at t x is alive at t](t)](j) 3. MP {PAST} t ( ) [λx.x is asleep at t x is alive at t](j) 4. MP {PAST} t ( ) j is asleep at t j is alive at t 5. MP {PAST} t j is asleep at t j is alive at t 6. t Y {PAST}[ P(John be.y asleep)] j is asleep at t j is alive at t 7. t P(John be.past asleep) j is asleep at t j is alive at t 8. t (t < t 0 ) 9. t j is asleep at t j is alive at t (t < t 0 )

26 John is asleep. 1. MP {PAST} ( John is asleep ). ( ) j is asleep at t j is alive at t 5. MP {PAST} t. 9. t j is asleep at t j is alive at t (t < t 0 )

27 Distributivity (Link, 1987; Lasersohn, 1998; i.a.) John and Bill are tall. My uncles are blond. All the dogs are asleep. Distributivity (D): D = λs.λp. x S[P(S)], where S is the set of entities denoted by the plural or conjoined nominal

28 John and Bill are tall. 1. [ D ( John and Bill )]( tall ( PRESENT (t))) 2. [[λs.λp. x S[P(x)]({j, b})]([λt.λx.x is tall at t and x is alive at t](t)) 3. [λp. x {j, b}[p(x)]](λx. t[x is tall at t and x is alive at t]) t 1 [j is tall at t and j is alive at t) 4. t 2 [b is tall at t and b is alive at t)

29 Scope Ambiguity D(MP(... )) ( ) ( ) t1 [j is tall at t and j is alive at t] t2 [b is tall at t and b is alive at t] MP MP MP(D(... )) ( t1 [j is tall at t and j is alive at t] MP t ) 2[b is tall at t and b is alive at t]

30 Scope Ambiguity D(MP(John and Bill... )): 1. x {j, b}[mp[... x...]] 2. MP[... j...] MP[... b...] 3. [... j...] t j < t 0 [... b...] t b < t 0 MP(D(John and Bill... )): 1. MP[ x {j, b}[... x...]] 2. MP[[... j...] [... b...] [... j...] [... b...] 3. (t j < t 0 t b < t 0 )

31 Scope Ambiguity D(MP(John and Bill... )): John Bill t 0 Bill John t 0 John Bill John is alive and Bill is alive. MP(D(John and Bill... )): t 0 John Bill Bill John t 0 John Bill Bill John John and Bill can t both be dead. John Bill John Bill t 0 t 0

32 Scope Ambiguity D(MP(2012 and )): 1. x {2012, 2016}[MP[... x...]] 2. MP[ ] MP[ ] 3. [ ] 2012 < t 0 [ ] 2016 < t 0 MP(D(2012 and )): 1. MP[ x {2012, 2016}[... x...]] 2. MP[[ ] [ ] [ ] [ ] 3. (2012 < t < t 0 )

33 Scope Ambiguity D(MP(2012 and )): t is present and 2016 is present. MP(D(2012 and )): t t t t and 2016 can t both be present t

34 Scope Ambiguity D(MP(... )) MP(D(... )) Individual John Bill #John and Bill are tall. t 0 John Bill Bill John John and Bill are tall. t t Interval 0 #2012 and 2016 are leap years and 2016 are leap years. t 0

35 Lifetime Parameter Updates D(MP(... )) MP(D(... )) Individual Interval 0 0 Maxim: Make your contribution presuppose as much as possible.

36 Lifetime Parameter Updates D(MP(... )) MP(D(... )) Individual John Bill t 0 2 John Bill Bill John t 0 John Bill Bill John t 0 John Bill John Bill t 0 Interval 0 0

37 Strongest Meaning Hypothesis Dalrymple, et al. (1998): Reciprocals Winter (2001): Plural predication Chierchia, et al. (2008): Exhaustification

38 Strongest Update Hypothesis If a sentence is ambiguous between two or more interpretations resulting in different numbers of individual lifetime parameter updates, the interpretation resulting in the greatest number of updates is preferred.

39 Lifetime Parameter Updates D(MP(... )) MP(D(... )) Individual # JFK and Joe Kennedy are tall. # My uncles are blond. # John and Bill are both very handsome. (Mittwoch, 2008) 0 Interval and 2016 are leap years. The even-numbered days this week are school days. Every Tuesday this month I fast. (Sauerland, 2002)

40 Summary Systematic difference between contexts in which present is vacuous and those in which it is non-vacuous Traditional theory & Sauerland s (2002) vacuous present fail to account for both Apparent irreconcilability can be resolved by: Admitting a formal realization of the distinction between individuals and intervals Assuming a covert operator analysis of distributivity and presupposition maximization with scope ambiguity Positing disambiguation based on a preferential interpretation principle sensitive to the difference

41 Questions MP as mobile grammatical operator vs. utterance-level maxim Intervals > Events > Individuals 2012 and 2016 are leap years.? The 87 th and 88 th Annual Meetings are in snowy climates. # JFK and Joe Kennedy are tall. Covert aspectual operators? (Thomas, 2012; i.a.) Futurate ( The Red Sox play the Yankees tomorrow. ) Habitual ( Every Christmas I go to California. ) [Calendrical? ( Next Friday is a holiday. ; 2016 is a leap year. )]

42 Thank you! Uli Sauerland Isabelle Charnavel Chrissy Zlogar

43 References Carlson, G. N. (1977). Reference to kinds in English. Ph.D. Dissertation, UMass Amherst. Chierchia, G., Fox, D., & Spector, B. (2008). The grammatical view of scalar implicatures and the relationship between semantics and pragmatics. Unpublished manuscript. Dalrymple, M., Kanazawa, M., Kim, Y., Mchombo, S., & Peters, S. (1998). Reciprocal expressions and the concept of reciprocity. Linguistics and Philosophy, 21(2), Hawkins, J. A. (1991). On (in) definite articles: implicatures and (un) grammaticality prediction. Journal of linguistics, 27(2), Heim, I. (1991). Artikel und definitheit. Semantik: ein internationales Handbuch der Zeitgen ossischen forschung, Mittwoch, A. (2008). Tenses for the living and the dead: Lifetime inferences reconsidered. Theoretical and crosslinguistic approaches to the semantics of aspect, Musan, R. (1997). Tense, predicates, and lifetime effects. Natural Language Semantics, 5(3), Percus, O. (2006). Antipresuppositions. Theoretical and empirical studies of reference and anaphora: Toward the establishment of generative grammar as an empirical science, 52, 73. Sauerland, U. (2002). The present tense is vacuous. Snippets, 6, Schlenker, P. (2012). Maximize presupposition and Gricean reasoning. Manuscript, UCLA and Institute Jean-Nicod. Singh, R. (2011). Maximize Presupposition! and local contexts. Natural Language Semantics, 19(2), Thomas, Guillaume. (2012). Temporal implicatures. Doctoral dissertation, Massachusetts Institute of Technology. Winter, Yoad. (2001). Plural predication and the strongest meaning hypothesis. Journal of Semantics 18.4: