1. Phases, Strong Islands and Computational Nesting Valentina Bianchi & Cristiano Chesi University of Siena The 28th GLOW Colloquium 2005 Genève, 31 March - 2 April, 2005

2. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Outline  Data: Left branch islands and the connectedness effect  Kayne’s Connectedness Condition  The computational model (Chesi 2004)  Left-branch islands as computationally nested phases  Right-hand adjunct islands

3. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting (Kayne 1983; Pollard & Sag 1994, 182 ff.) (1)a. * [Which famous playwright] i did [close friends of e i ] become famous ? b. ? [Which famous playwright] i did [close friends of e i ] admire e i ? (2) a. * Who did [my talking to e i ] bother Hilary ? (Pollard & Sag 1994) b. √ Who did [my talking to e i ] bother e i ? (3) a. * Who i did you consider [friends of e i ] angry at Sandy ? b. √ Who i did you consider [friends of e i ] angry at e i ? (Pollard & Sag 1994) ■□□□□ Data: Left branch islands and the connectedness effect

4. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Left branch constituents are islands for extraction A legitimate gap on a right branch can “rescue” an illegitimate gap inside a left branch X eXeX eXeX eXeX ■□□□□ Data: Left branch islands and the connectedness effect

5. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting (5) Y is a g-projection of X iff i. Y is an ( X' ) projection of X or of a g-projection of X, or ii. X is a structural governor and Y immediately dominates W and Z, where Z is a maximal projection of a g-projection of X, and W and Z are in a canonical government configuration: (6) W and Z (Z a maximal projection, and W and Z immediately dominated by some Y) are in a canonical government configuration iff a. V governs NP to its right in the grammar of the language and W precedes Z b. V governs NP to its left in the grammar of the language and Z precedes W (7) The g-projection set G  of a category  is defined as follows (where  governs  ): a. ,  = a g-projection of     G  b.   G  and b'.  dominates  and  does not dominate     G  □■□□□ Kayne’s Connectedness Condition (Kayne 1983)

6. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting (8) Connectedness Condition Let  1...  j,  j+1...  n be a maximal set of empty categories in a tree T such that   j,  j is locally bound by . Then {  }  ( G  j ) must constitute a subtree of T.    nn □■□□□ Kayne’s Connectedness Condition 1 - all the maximal projections in the path between the gap and its binder are on a right branch or 2 - a path terminating in a left branch is connected to a legitimate path of right branches

7. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Which famous playwright become did close friends of ee famous  11 G1G1 (1) a. * □■□□□ Kayne’s Connectedness Condition

8. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Which famous playwright become did 1 close friends of 1 1 ee famous  1 1 (1) a. * 11 □■□□□ Kayne’s Connectedness Condition G1G1

9. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Which famous playwright admire did 1 close friends of 1 1 ee ee  1 1 22 (1) b. 11 □■□□□ Kayne’s Connectedness Condition G1G1

10. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting 2 Which famous playwright 2 admire 2 did 1 close friends of 1 1 ee ee 2  1 1 G2G2 2 2 (1) b. 11 □■□□□ Kayne’s Connectedness Condition 22 G1G1

11. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting who you because  (9) *a person who you admire e because [close friends of e] became famous admire ee became famous 1 close friends 1 G1G1 of 1 ee 11 1 1 □■□□□ Kayne’s Connectedness Condition

12. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting who 2 you because  (9) *a person who you admire e because [close friends of e] became famous admire 2 ee became famous 1 close friends 1 G1G1 of 1 ee 11 1 1 □■□□□ Kayne’s Connectedness Condition 22 G1G1 2 2 2

13. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting 1 - Kayne’s Connectedness Condition does not subsume right hand islands 2 - Nature of the parasitic gap: is it like any ordinary gap (as in HPSG), or is it an empty resumptive pronoun (Cinque 1990, Postal 1994)? Parasitic gaps has been claimed to differ from ordinary gaps w.r.t. restriction to the NP category incompatibility with antipronominal contexts lack of reconstruction effects (see Culicover & Postal (2001) and Levine & Sag (2003), for various positions). We remain neutral w.r.t. this question. For simplicity, we will assimilate the parasitic gap-antecedent dependency to the usual antecedent-gap dependency, and treat both in terms of copy-remerging. □■□□□ Kayne’s Connectedness Condition

14. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Generalization on legitimate recursion and gap licensing Legitimate gaps lie on the main recursive branch of the tree, whereas illegitimate gaps lie on “secondary” branches, which do not allow for unlimited recursion (in that such a secondary branch cannot be the lowest one in a tree). □■□□□ Kayne’s Connectedness Condition

15. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Competence Features Structures (semantic + syntactic/abstract + phonetic features → lexicon ) Structure Building Operations (merge, move, phase) Performance tasks ParsingGeneration Flexibility Universals Parameterization Economy conditions □□■□□ The computational model (Chesi 2004)

16. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Structure Building Operations (merge, move, phase) □□■□□ The computational model Structure Building Operations Structure Building Operations

17. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting □□■□□ The computational model the flexibility requirement implies a Top-to-bottom orientation of Structure Building Operations theoretical arguments: Phillips’ (1996) temporary constituency; Teleological movement in a bottom-to-top perspective; psycholinguistic evidence: incremental parsing: garden paths; incremental generation: false starts. Structure Building Operations

18. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting MERGE binary function (sensitive to temporal order) taking two features structures and unifying them. PHASE PROJECTION is the minimal set of dominance relations introduced in the SD based on the expectations triggered by each select feature of the currently processed lexical items MOVE top-down oriented function which stores an un-selected element in a memory buffer and re-merges it at the point of the computation where the element is selected □□■□□ The computational model Structure Building Operations

19. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting MOVE Linearization Principle (inspired by Kayne’s LCA) if A immediately dominates B, then either a. if A selects B as an argument, or b. if B is in a functional specification of A e.g. “the boy kissed the girl” PHASE the boy kissed [ =o kiss] [ =s =o kiss] [ +T kiss] [ =s =o kiss] Memory Buffer the boy Memory Buffer □□■□□ The computational model V head V V V V V V V Selected Phase(s) (select features)... (left periphery)... F1F1 FnFn Functional Sequence (licensor features) the girl

20. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Sequential Phase Nested Phase Vs. FnFn S last head Memory Buffer F1F1 S1S1 Memory Buffer Memory Buffer FnFn S last head F1F1 S1S1 Memory Buffer Success Condition: the memory buffer must be empty at the end of the phase or else its content is inherited by the memory buffer of the next sequential phase (if any) □□■□□ The computational model

21. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting To summarize: 1.Every computation is a top-down process divided into phases. 2.A phase gets closed when the last selected complement of its head is processed; this last projected complement constitutes the next sequential phase. 3.All unselected constituents are instead nested phases: they are processed while the superordinate phase has not been closed yet. 4.The Move operation stores an unselected element found before (i.e. on the left of) the head position in the local memory buffer of the current phase, and discharges it in a selected position if possible; if not, when the phase is closed the content of the memory buffer is inherited by that of the next sequential phase. □□■□□ The computational model

22. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting □□□■□ Strong islands as computationally nested phases (10)Who i do you believe [t who that everybody admires t who ]? Who believe do you you = 2 nd Nested Phase (DP) Matrix Phase (CP) Memory Buffer (Matrix Phase, CP) who = 1 st Nested Phase (DP) who you V Sel. Lic. that everybody admires who that = Selected Phase (CP)

23. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Who become did close friends of _ G  1 = 2 nd Nested Phase (DP) V Matrix Phase (CP) Sel. Lic. Memory Buffer (Matrix Phase, CP) who = 1 st Nested Phase (DP) who famous □□□■□ Strong islands as computationally nested phases (1.a) *Who i did [close friends of e i ] become famous ? e

24. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Who admire did close friends of e G  1 = 2 nd Nested Phase (DP) V G  = Matrix Phase (CP) Sel. Lic. Memory Buffer (Matrix Phase, CP) who = 1 st Nested Phase (DP) who G1G1 <G1><G1> who □□□■□ Strong islands as computationally nested phases (1.b) ? Who i did [close friends of e i ] admire e i ?

25. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Who become did close friends of e G  1 = 2 nd Nested Phase (DP) V Matrix Phase (CP) Sel. Lic. Memory Buffer (Matrix Phase, CP) who = 1 st Nested Phase (DP) who G1G1 <G1><G1> who □□□■□ Strong islands as computationally nested phases famous (1.a) *Who i did [close friends of e i ] become famous ?

26. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Summary of the proposed analysis We have recast the Connectedness Condition in derivational terms, by assuming: (a)a top-to-bottom derivation divided in phases (b)a “storage” conception of the Move operation (c)a distinction between sequential and nested phases (corresponding to branches on the recursive vs. non-recursive side of the tree). (d)The content of the memory buffer of a phase can only be inherited by the next sequential phase, and not by a nested phase. (e) Parasitic gaps exploit the possibility of “parasitically” copying the content of the buffer of a matrix phase into the buffer of a nested phase. (f)Parasitic copying, however, cannot empty the matrix memory buffer, whence the necessity of another (“legitimate”) gap within the matrix phase itself (or within a phase that is sequential to the matrix one). □□□■□ Strong islands as computationally nested phases

27. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting □□□□■ Right hand adjunct islands as nested phases (11)a. ?? [Those boring old reports] i, Kim went to lunch [without reading e i ]. b. √ [Those boring old reports] i, Kim filed e i [without reading e i ]. (12) ? [A person] i that they spoke to e i [because they admire e i ] Longobardi (1985) strenghtens the notion of g-projection, by adding a proper government requirement: a non properly governed maximal projection is a boundary to the extension of g-projections. By definition, subjects and adjuncts are not properly governed: thus, the adjunct island is assimilated to the subject island, much as in Huang’s (1982) Condition on Extraction Domains.

28. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Those boring old reports Kim 1 without PRO 1  GG (11.a) ?? [Those boring old reports] i, Kim went to lunch [without reading e i ]. went to lunch reading 1 ee 11 1 1 □□□□■ Right hand adjunct islands as nested phases

29. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting 2 Those boring old reports 2 2 Kim 1 without PRO 1 ee  GG (11.b)[Those boring old reports] i, Kim filed e i [without reading e i ]. filed 2 2 reading 1 ee 11 1 1 22 □□□□■ Right hand adjunct islands as nested phases

30. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Problem 1: Not all right-hand adjuncts are equally strong islands (cf. a.o. Pollard & Sag 1994, 191 and Haider 2003): (13) a. Who did you go to Girona [in order to meet e]? b.This is the blanket that Rebecca refuses to sleep [without e]. c. How many of the book reports did the teacher smile [after reading e]? (Pollard & Sag 1994) (14) a. the car that he left his coat [in e] b. the day that she was born [on e] c. * the day that she was born in England [on e] (Haider 2003, 3) Then it is not obvious that “adjunct islands” should be assimilated to “left branch islands” (Pollard & Sag 1994); minimally we should distinguish “true adjuncts” from “oblique complements” □□□□■ Right hand adjunct islands as nested phases

31. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Problem 2: Right-hand relative clauses are, prima facie, another instance of a non properly governed maximal projection (cf. Complex NP Island Constraint): (15) ? * Which book did John meet [ NP a child [ CP who read t]] But a subject complex NP allows for the extension of g-projections in a connectedness configuration: (16)a. * A person who [people that talk to e i ] usually have money in mind b. ? A person who [people that talk to e i ] usually end up fascinated with e i □□□□■ Right hand adjunct islands as nested phases

32. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting A person with who 1 people that e 1 1 ee  GG 1 (16) b. ? usually end up fascinated to 1 ee 1 1 talk 11 22 □■□□□ Kayne’s Connectedness Condition

33. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting 2 A person 2 with 2 who 1 people that e 1 1 ee 2  GG 1 (16) b. ? usually 2 end up 2 fascinated 2 to 1 ee 1 1 talk 2 22 □■□□□ Kayne’s Connectedness Condition 11

34. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Longobardi (1985) must modify his definition of proper government so that the relative clause counts as properly governed; but then, the Complex NP Island Constraint must be stipulated as a separate constraint on extraction. (The Complex NP Island Constraint also did not follow from Kayne’s original Connectedness Condition, since it is a right branch: cf. Kayne 1984, n. 5.) The variable strenght of adjunct islands and the unresolved status of the (relative clause) Complex NP Island in the connectedness approach cast some doubt on the idea that right-hand adjuncts must be completely assimilated to left branch islands, as in Longobardi’s approach (cf. Pollard & Sag 1994, Levine & Sag 2003 for a similar conclusion). □□□□■ Right hand adjunct islands as nested phases

35. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Memory Buffer FnFn C last head F1F1 C1C1 Memory Buffer □□□□■ Right hand adjunct islands as nested phases Nested Phase Memory Buffer FnFn C last head F1F1 C1C1 Memory Buffer

36. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Memory Buffer X C last head F1F1 C1C1 Memory Buffer □□□□■ Right hand adjunct islands as nested phases Nested Phase Memory Buffer FnFn C last head F1F1 C1C1 Memory Buffer [ =x F n ]

37. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Memory Buffer X [ =x head] F1F1 C1C1 □□□□■ Right hand adjunct islands as nested phases Nested Phase Memory Buffer FnFn C last head F1F1 C1C1 Memory Buffer FnFn C2C2

38. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting Who admire you you = Nested Phase V G  = Matrix Phase Memory Buffer (Matrix Phase, CP) who = Nested Phase who you because close friends of _ became famous because = Nested Phase G  1 = Doubly-nested Phase 11 (9) *... Who you admire e because [close friends of e] became famous □□□□■ Right hand adjunct islands as nested phases who

39. Bianchi, Chesi - Phases, Strong Islands, and Computational Nesting 1.Novel account of recursive/transparent phases (at least in VO languages) depending on select features of the previous phase-head, in particular: a.Left branch islands are computationally nested phases (selected phases are on the right of the head, cf. Linearization Principle) b.Right hand adverbials too can be analyzed as computationally nested phases, depending on the structure of the relevant licensor feature 2.Extending the Top-to-Bottom orientation (Phillips 1996) to Move and to Phase Projection allows us to capture (a subset of) Strong Islands effects and the related connectedness effects in a derivational way 3.These results directly follow from a conception of the competence that includes Structure Building Operations fulfilling the flexibility requirement Conclusions

40. Phases, Strong Islands and Computational Nesting Valentina Bianchi bianchi10@unisi.it Cristiano Chesi cri@mit.edu http://www.ciscl.unisi.it