Introduction to Lexicalized Tree Adjoining Grammar (LTAG)

Transcription

1 Introduction to Lexiclized Tree Adjoining Grmmr (LTAG) Mchine Lnguge Processing eminr Jnury 29,

2 Tble of Contents 1.The forml relevnce of LTAG: From CFG to LTAG - Context-Free Grmmrs (CFG) - Lexicliztion, try 1: Tree ubstitutions Grmmrs - Lexicliztion, try 2: Tree Adjoining Grmmrs - ome forml properties of LTAG 2. The linguistic relevnce of LTAG -LTAG - Loclity of dependencies - hpe of dependencies - Psycholinguistic processing 2

3 Tble of Contents 1. The forml relevnce of LTAG: From CFG to LTAG - Context-Free Grmmrs (CFG) - Lexicliztion, try 1: Tree ubstitutions Grmmrs - Lexicliztion, try 2: Tree Adjoining Grmmrs - ome forml properties of LTAG 2. The linguistic relevnce of LTAG 3

4 Context-Free Grmmrs CFG G 1 : s set of rewrite rules, or NP V VP V NP NP John/Mry V loves s primitive building blocks or trees VP N N V NP VP VP NP John Mry loves 4

5 Context-Free Grmmrs CFG G 1 : NP V VP V NP NP John/Mry V loves VP N N V NP VP VP NP John Mry loves The domin of loclity is the one-level tree. Thus, the rguments of the predicte re not in the sme domin. The locl domins re not ll lexiclized. 5

6 Lexicliztion New notion of lexicl item: n elementry structure ( tree, i.e. directed cyclic grph) with lexicl nchor or word This notion of lexicl item gives us domin of loclity -the corresponding tree- in which ll syntctic (e.g. greement) nd semntic dependencies (e.g. predicte-rgument reltions) re encpsulted (Frnk 2002) Complicte loclly, simplify globlly 6

7 Lexicliztion, Try 1: Tree ubstitution Grmmrs (TG) CFG G 1 : NP V VP V NP NP John/Mry V loves TG G 1 : α 1 α 2 NP α 3 NP VP John Mry V loves 7

8 Lexicliztion, Try 1: Tree ubstitution Grmmrs (TG) ubstitution: α: β: X X γ: X β 8

9 Lexicliztion, Try 1: Insufficiency of TG CFG G 2 : γ : TG G 2 : α 1 : α 2 : α 3 : Question: Cn the CFG G 2 generte the tree γ? Question: Cn the TG G 2 generte the tree γ? 9

10 Lexicliztion, Try 1: Insufficiency of TG CFG G 2 : TG G 2 : α 1 : α 2 : Question: Cn the CFG G 2 generte the tree γ? Yes Question: Cn the TG G 2 generte the tree γ? No α 3 : γ : Hence: CFGs cnnot be lexiclized by TGs, tht is, only by substitution. 10

11 Lexicliztion, Try 2: Tree Adjoining Grmmrs (TAG) Adjoining: α: X β: X X* γ: X X β We djoin tree β to tree α t the node lbeled X. 11

12 Lexicliztion, Try 2: Tree Adjoining Grmmrs (TAG) CFG G 2 : γ : TG G 22 : α 1 : α 2 : α 3 : Question: Cn the CFG G 2 generte the tree γ? Yes Question: Cn the TAG G 22 generte the tree γ? 12

13 Lexicliztion, Try 2: Tree Adjoining Grmmrs (TAG) CFG G 2 : TG G 22 : α 1 : α 2 : Question: Cn the CFG G 2 generte the tree γ? Yes α 3 : γ : Question: Cn the TAG G 22 generte the tree γ? Yes CFGs cn be lexiclized by LTAGs ( L(CFG) L(TAG ) nd T(CFG) T(TAG) ). Adjoining is crucil for lexicliztion. 13

14 ome forml properties of LTAG (Joshi nd chbes 1997) TAGs re more powerful thn CFGs, both wekly nd strongly, i.e., in terms of the set of string they chrcterize: L(CFG) L(TAG) the set of derived trees they support:t(cfg) T(TAG) 14

15 ome forml properties of LTAG TAGs re mildly context-sensitive grmmrs Turing-cceptble lgs Context sensitive lgs Mildly context-sensitive lgs Context free lgs Regulr lgs 15

16 ome forml properties of LTAG TAGs crry over ll forml properties of CFGs, modified in the pproprite wy polynomil prsing: n 6 for TAG, n 3 for CFG TAGs correspond to Embedded Pushdown Automt (EPDA) in the sme wy s PDAs correspond to CFGs (Vijy-hnker 1987) 16

17 Tble of Contents 1. The forml relevnce of LTAG: from CFG to LTAG 2. The linguistic relevnce of LTAG -LTAG - Loclity of dependencies - hpe of dependencies - Psycholinguistic processing 17

18 Lexiclized TAG (LTAG) Finite set of elementry trees, ech with lexicl nchor, encpsulting the syntctic nd semntic dependencies of tht nchor. Two types of elementry trees: initil nd uxiliry. Two syntctic opertions: - ubstitution: replcing leve with new tree - Adjunction: replcing n internl node with tree 18

19 Lexiclized TAG (LTAG) Derivtion: The result of crrying out the substitutions nd djunctions is the derived tree. yntctic output The history of how elementry trees re put together is recorded in the derivtion tree. Input to semntics 19

20 (1) John sometimes lughs NP John VP ADV VP* sometimes VP V lughs Derived tree: NP VP John ADV VP Derivtion tree: lughs np vp john sometimes sometimes V lughs 20

21 Loclity of dependencies 1/10 Predicte-rgument reltions: ll rguments of the lexicl nchor re loclized Agreement: person, number, gender Idioms filler-gp constructions: (1) Who does Bill think Hrry likes Etc. 21

22 Loclity: Filler-gp 2/10 A fmily of trees for likes: object wh-extrction, subject wh-extrction, object reltive, subject reltive, pssive, topicliztion, V likes VP V VP trnsitive likes e object extrction 22

23 Loclity: Filler-gp 3/10 (1) [Who] NP (does) Hrry like e V VP likes e who Hrry 23

24 Loclity: Filler-gp 4/10 (2) [Who] NP (does) Bill think Hrry likes e V VP VP V * think likes e who Hrry Bill 24

25 Loclity: Filler-gp 5/10 (2) [Who] NP (does) Bill think Hrry likes e NP who NP Bill VP V think NP V Hrry likes VP NP e 25

26 Loclity: Filler-gp & islnds 6/10 Islnds: domins from which extrction is impossible. Exmples: Wh-cluse: (1) * Wht NP does Bill wonder who likes e? Reltive cluse: (2) * Wht NP does Bill see the boy who likes e? Noun complement cluse: (3) *Wht NP did Bill her the clim tht Hrry likes e? Adverbil cluse: (4) * Wht NP did you fll sleep when you were reding e? ententil subject: (5) * Wht NP did [tht I like e] bother you? 26

27 Loclity: Wh-cluse islnd 7/10 (1) * Wht NP (does) Bill wonder who NP e likes e? VP VP V * wonder e V likes wht who Bill e 27

28 Loclity: Wh-cluse islnd 8/10 (1) is ruled out for the sme reson tht (2) nd (3) re out in English: multiple wh-extrction is simply disllowed, regrdles of islnds. Tht is, the initil tree of likes with multiple wh-extrction is not prt of the grmmr of English (Kroch nd Joshi 1985). (1) * Wht NP (does) Bill wonder who NP e likes e? (2) * Wht NP (does) who NP e like e? (3) * Wht NP [to who] PP did John give e e? 28

29 Loclity: Wh-cluse islnd 9/10 Prediction: lnguges tht llow multiple whextrction should lso llow for extrction out of wh-islnd: Romnin (1) Cine despre ce e mi- povestit e? Who bout wht me-hs told Who told me bout wht? (2) Cine stii despre ce e i- povestit e? Who you-know bout wht him-hs told Who do you know wht e hs told him bout? 29

30 Loclity: Reltive cluse islnd 10/10 Object reltive tree for likes: NP NP VP V NP Question: How cn we rule out extrction out of reltive cluse islnd, s in (1)? (1) * Wht NP does Bill see the boy who likes e? likes e 30

31 hpe of dependencies 1/10 Depending on the rchitecture of the elementry trees, TAG grmmr cn generte: nested dependencies nd crossed dependencies 31

32 Nested Dependencies 2/10 TAG G 3 : b b 32

33 Nested Dependencies 3/10 TAG G 3 : b b b b b b b b Liner structure: Nested dependencies 33

34 Crossed Dependencies 4/10 TAG G 4 : b * b 34

35 Crossed Dependencies 5/10 TAG G 4 : b * b b b b Liner structure: crossed dependencies b 35

36 Crossed Dependencies 6/10 TAG G 4 : Dependencies re nested on the tree b * b b b Liner structure: crossed dependencies b b 36

37 Exmples: Nested Dependencies 7/10 Embedding of reltive cluses in English (1) The ct 1 the boy 2 chsed 2 jumped 1. Embedding of complement cluses in Germn (2) Hns 1 Peter 2 Mrie 3 schwimmen 3 lssen 2 sh 1 Hns sw Peter mke Mrie swim 37

38 Exmples: Crossed Dependencies 8/10 Embedding of complement cluses in Dutch (3) Jn 1 Piet 2 Mrie 3 zg 1 lten 2 zwemmen 3 Hns sw Peter mke Mrie swim 38

39 Exmples: Mixed dependencies 9/10 It is possible to obtin mixed dependencies, tht is, complex combintions of nested nd crossed dependencies. This type of ptterns re ttested, for exmple, in scrmbling nd clitic movement. 39

40 Exmples: Mixed dependencies 10/10 Question: Define n LTAG (i.e., spell out the elementry trees) tht cn generte the mixed dependency below: b b b 40

41 Psycholinguistic processing Prepositionl Phrse mbiguities: (1) The spy sw the cop with telescope (2) The secretry of the generl with red hir In (1) the mbiguity is lexicl in the LTAG sense In (2) the mbiguity is structurl, resolved t the level of syntctic composition The mbiguity in sentences like (1) follows the ptterns of lexicl dismbigution in humn processing dt (Kim, rinivs nd Trueswell )

42 References Frnk, R Phrse tructure Composition nd yntctic Dependencies. Cmbridge, Mss: MIT Press. Hn, C.-H Introduction to TAG-bsed Linguistic Theory. At Joshi, A.K trting with complex primitives pys off: Complicte Loclly, implify Globlly. lides for Cogci Joshi, A.K. nd chbes, Y Tree-Adjoining Grmmrs, in G. Rozenberg nd A. lom (eds.), Hndbook of Forml Lnguges, pringer, Berlin, New York, pp Kim, A., rinivs, B. & Trueswell, J.C The convergence of lexiclist perspectives in psycholinguistics nd computtionl linguistics, in P. Merlo nd. tevenson (eds.). entence Processing nd the Lexicon: Forml, Computtionl nd Experimentl Perspectives, John Benjmins Publishing, pp Kroch, A. nd A. Joshi The linguistic relevnce of TAGs. University of Pennsylvni Technicl Report, M-CI Vijy-chnker, K A study of Tree Adjoining Grmmrs. Ph.D. thesis, CI, Univ. Pennsylvni. 42