1. October 25, 2006 11-721: Grammars and Lexicons Lori Levin

2. Lexical Functional Grammar History: –Joan Bresnan (linguist, MIT and Stanford) –Ron Kaplan (computational psycholinguist, Xerox PARC) –Around 1978

3. What is Linguistic Theory Delimit the range of possible human languages. –What do all languages have in common? Semantic roles, grammatical relations, pragmatic relations, some constituent structure; only subjects can be controllees in matrix coding as subject constructions; etc. –What are the ways in which they can differ from each other? Relative prominence of grammatical or pragmatic relations: word order reflects grammatical relations in English and reflects focus (new information) in Hungarian; Topic takes precedence over subject in Chinese in determining antecedent of null pronouns; Subject is more prominent in English. –What never happens in a human language? Make a question by saying the sentence backwards.

4. Universalist view of language There is “a common organizing structure of all languages that underlies their superficial variations in modes of expression” (Bresnan) –E.g., Passives that look very different in different languages can be described by a universal passive rule. The common organizing structure is part of human biology.

5. Some differences between English and Warlpiri The two small children are chasing that dog. Aux V NP NP VP VP’ S Wita-jarra-rlu ka-pala wajili-pi-nyi yalumpu kurdu-jarra-rlu maliki. Small- DU-ERG pres- 3duSUBJ chase- NPAST that.ABS child- DU-ERG dog.ABS NP AUX V NP NP NP S

6. Possible word orders in Warlpiri that are not possible in English *The two small are chasing that children dog. *The two small are dog chasing that children. *Chasing are the two small that dog children. *That are children chasing the two small dog.

7. Non-configurational languages Free word order. May have discontinuous constituents. Tests for constituency do not yield evidence for VP constituent.

8. Something that English and Warlpiri have in common Lucy is hitting herself. *Herself is hitting Lucy. Napaljarri-rli ka-nyanu paka-rni Napaljarri- ERG PRES-REFL hit- NONPAST “Napaljarri is hitting herself.” *Napaljarri ka-nyanu paka-rni Napaljarri. ABS PRES-REFL hit- NONPAST “Herself is hitting Napaljarri.”

9. What English and Warlpiri have in common according to Chomsky NP VP VP’ S Aux V NP Deep structure NP VP VP’ S Aux V NP Surface Structure English

10. What English and Warlpiri have in common according to Chomsky NP VP VP’ S Aux V NP Deep structure Surface Structure Warlpiri S NP Aux V NP NP NP

11. What English and Warlpiri have in common according to Bresnan Same grammatical relations and semantic roles –SUBJECT: the two small children: AGENT –PREDICATE: are chasing –OBJECT: that dog: PATIENT Different codings of grammatical relations: –English subject: NP immediately under S –Warlpiri subject: Ergative case marked NP (if verb is transitive)

12. Strength of Chomsky’s approach Proposing that there is a VP in all languages explains why there are subject-object asymmetries in all languages.

13. Strength of Bresnan’s approach Doesn’t propose non-existent VPs: –phrase structure is used for representing constituency –A different representation is used for grammatical relations

14. Challenges for Bresnan and Chomsky Bresnan: –explain subject-object asymmetries in the absence of a VP –Explain in a principled way the range of possible coding properties of grammatical relations Chomsky: –explain in a principled way how the words get scrambled out of VP; –The phrase structure tree has to represent both grammatical relations and constituent structure, which may conflict with each other.

15. Levels of Representation in LFG [ s [ np The bear] [ vp ate [ np a sandwich]]] constituent structure SUBJ PRED OBJ functional structure Agent eat patient thematic roles Grammatical encoding Lexical mapping Eat lexical mapping SUBJ OBJ S NP SUBJ VP V NP OBJ VP V PP OBL Grammatical Encoding For English!!!

16. Syntax Syntax is not about the form (phrase structure) of sentences. It is about how strings of words are associated with their semantic roles. –Phrase structure is only part of the solution. Sam saw Sue –Sam: perceiver –Sue: perceived

17. Syntax Syntax is also about how to tell that two sentences are thematic paraphrases of each other (same phrases filling the same semantic roles). –It seems that Sam ate the sandwich. –It seems that the sandwich was eaten by Sam. –Sam seems to have eaten the sandwich. –The sandwich seems to have been eaten by Sam.

18. How to associate phrases with their semantic roles in LFG Starting from a constituent structure tree: Grammatical encoding tells you how to find the subject. –The bear is the subject. Lexical mapping tells you what semantic role the subject has. –The subject is the agent. –Therefore, the bear is the agent.

19. Levels of Representation in LFG [ s [ np The sandwich ] [ vp was eaten [ pp by the bear]]] constituent structure SUBJ PRED OBL functional structure patient eat agent thematic roles Grammatical encoding Lexical mapping Eat lexical mapping OBL SUBJ S NP SUBJ VP V NP OBJ VP V PP OBL Grammatical Encoding For English!!!

20. Active and Passive Active: –Patient is mapped to OBJ in lexical mapping. Passive –Patient is mapped to SUBJ in lexical mapping. Notice that the grammatical encodings are the same for active and passive sentences!!!

21. Passive mappings Starting from the constituent structure tree. The grammatical encoding tells you that the sandwich is the subject. The lexical mapping tells you that the subject is the patient. –Therefore, the sandwich is the patient. The grammatical encoding tells you that the bear is oblique. The lexical mapping tells you that the oblique is the agent. –Therefore, the bear is the agent.

22. How you know that the active and passive have the same meaning In both sentences, the mappings connect the bear to the agent role. In both sentences, the mappings connect the sandwich to the patient role (roll?) In both sentences, the verb is eat.

23. Levels of Representation in LFG [s-bar [ np what ] [s did [ np the bear] eat ]] constituent structure OBJ SUBJ PRED functional structure patient agent eat thematic roles Grammatical encoding Lexical mapping Eat lexical mapping SUBJ OBJ VP V PP OBL Grammatical Encoding For English!!! S NP SUBJ S-bar NP OBJ S

24. Wh-question Different grammatical encoding: –In this example, the OBJ is encoded as the NP immediately dominated by S-bar Same lexical mappings are used for: –What did the bear eat? –The bear ate the sandwich.

25. Principles Variability: –Phrase structures and grammatical encodings vary across languages. Universality –Functional structures are largely invariant across languages.

26. Functional Structure SUBJ PRED ‘bear’ NUM sg PERS 3 DEF + PRED ‘eat SUBJ OBJ TENSE past OBJ PRED ‘sandwich’ NUM sg PERS 3 DEF -

27. Functional Structure Pairs of attributes (features) and values –Attributes (in this example): SUBJ, PRED, OBJ, NUM, PERS, DEF, TENSE –Values: Atomic: sg, past, +, etc. Feature structure: [num sg, pred `bear’, def +, person 3] Semantic form: ‘eat ’, ‘bear’, ‘sandwich’

28. Semantic Forms Why are they values of a feature called PRED? –In some approaches to semantics, even nouns like bear are predicates (function) that take one argument and returns true or false. –Bear(x) is true when the variable x is bound to a bear. –Bear(x) is false when x is not bound to a bear.

29. Why is it called a Functional Structure? X squared1 24 39 416 525 Each feature has a unique value. featuresvalues Also, another term for grammtical relation is grammatical function.

30. We will use the terms functional structure, f-structure and feature structure interchangeably.

31. Give a name to each function SUBJ PRED ‘bear’ NUM sg PERS 3 DEF + PRED ‘eat SUBJ OBJ TENSE past OBJ PRED ‘sandwich’ NUM sg PERS 3 DEF - f1 f2 f3

32. How to describe an f-structure F1(TENSE) = past –Function f1 applied to TENSE gives the value past. F1(SUBJ) = [PRED ‘bear’, NUM sg, PERS 3, DEF +] F2(NUM) = sg

33. Descriptions can be true or false F(a) = v –Is true if the feature-value pair [a v] is in f. –Is false if the feature-value pair [a v] is not in f.

34. This is the notation we really use (f1 TENSE) = past Read it this way: f1’s tense is past. (f1 SUBJ) = [PRED ‘bear’, NUM sg, PERS 3, DEF +] (f2 NUM) = sg

35. Chains of function application (f1 SUBJ) = f2 (f2 NUM) = sg ((f1 SUBJ) NUM) = sg Write it this way. (f1 SUBJ NUM) = sg Read it this way. “f1’s subject’s number is sg.”

36. More f-descriptions (f a) = v –f is something that evaluates to a function. –a is something that evaluates to an attribute. –v is something that evaluates to a function, symbol, or semantic form. (f1 subj) = (f1 xcomp subj) –Used for matrix coding as subject. A subject is shared by the main clause and the complement clause (xcomp). (f1 (f6 case)) = f6 –Used for obliques

37. Lions seem to live in the forest DET N P NP V PP COMP VP N V VP-bar NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF +

38. Lions seem to live in the forest DET N P NP V PP COMP VP N V VP-bar NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + f1 f3 f2 f4 f5 f6 n7 n6 n5 n4 n3 n2 n1 n10 n9 n8 n11 n13 n12 n14

39. Lions seem to live in the forest DET N P NP V PP COMP VP N V VP-bar NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + f1 f3 f2 f4 f5 f6 n7 n6 n5 n4 n3 n2 n1 n10 n9 n8 n11 n13 n12 n14

40. Properties of the mapping from c- structure to f-structure Each c-structure node maps onto at most one f- structure node. More than one c-structure node can map onto the same f-structure node. An f-structure node does not have to correspond to any c-structure node. (But the information it contains does come from somewhere – either a grammar rule or lexical entry.)

41. Φ is a mapping from c-structure nodes to f- structure nodes. –There are other mappings to semantic structures, argument structures, discourse structures,etc. * is the “current” c-structure node (me). Φ(*) is “my f-structure” (  ) m(*) is “my c-structure mother” Φ(m(*)) is “my c-structure mother’s f-structure” (  ) The formalism for grammatical encoding : Local co-description of partial structures

42. Local co-description of partial structures S  NP VP (  SUBJ) =   =  NP says: My mother’s f-structure has a SUBJ feature whose value is my f-structure. VP says: My mother’s f-structure is my f-structure. This rule simultaneously describes a piece of c- structure and a piece of f-structure. It is local because each equation refers only to the current node and its mother. (page 119-120)

43. Other types of equations F-structure composition –(  SUBJ NUM) = sg –My f-structure has a subj feature, whose value is another f-structure, which has a num feature, whose value is sg. –Usually, path names are not longer than two. Two features pointing to the same value: –(  SUBJ) = (  XCOMP SUBJ) –(  SUBJ) = (  TOPIC) (  (  CASE)) =  (Dalrymple pages 152-153) –Sam walked in the park. –(  CASE) = OBL-loc –(  OBL-loc) = 

44. The minimal solution The f-structure for a sentence is the minimal f-structure that satisfies all of the equations. (page 101).

45. Building an F-structure: informal, for linguists Annotate –Assign a variable name to the f-structure corresponding to each c-structure node. –May find out later that some of them are the same. Instantiate –Replace the arrows with the variable names. Solve –Locate the f-structure named on the left side of the equation. –Locate the f-structure named on the right side of the equation –Unify them. –Replace both of them with the result of unification.

46. Unification [], empty feature structure, is identity element –[] U x = x Atomic value unified with an atomic value: –x U x = x –x U y = fail Atomic value unified with a non empty feature structure: fail

47. Unification Feature structure f1 unified with feature structure f2 to make feature structure f3: –The set of features is the union of the features in f1 and f2. –The value of each feature in f3 is the value of that feature in f1 unified with the value of that feature in f2. –Keep going recursively if there are embedded feature structures. –If any unification fails, then the whole thing fails.

48. Unification and Grammaticality Grammatical sentence: –All unifications succeed and –Phrase structure derivation succeeds Ungrammatical sentence: –At least one unification fails or –Phrase structure derivation fails

49. Unification Example f1 [ num sg gender masc person 3] f2 [ case nom def + person 3] f3 [ num sg gender masc person 3 case nom def +]

50. Unification Example f1 [ num sg gender masc person 3] f2 [ case nom def + person 2] Unification fails. No f- structure is produced.

51. Unification Example f1 [ subj [num sg gender masc person 3] tense pres] f2 [ subj [case nom def + person 3] tense pres neg +] f3 [ subj [num sg gender masc person 3 case nom def +] tense pres neg +]

52. Unification Example f1 [ subj [num sg gender masc person 2] tense pres] f2 [ subj [case nom def + person 3] tense pres neg +] Unification fails. No f- structure is produced.

53. Lions seem to live in the forest DET N P NP V PP COMP VP N V VP-bar NP f2 VP f3 S f1 SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + Rule: S → NP VP (↑ SUBJ) = ↓ ↑=↓ (↑VFORM) = fin Instantiated equations: (f1 SUBJ) = f2 f1 = f3 f1 f2 f3

54. Lions seem to live in the forest DET N P NP V PP COMP VP f4 N f5 V VP-bar NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + lion: N seem: V (↑ PRED) = `lion’ (↑ PRED) = ‘seem SUBJ’ XCOMP (↑ SUBJ) = (↑ XCOMP SUBJ) -s (suffix for nouns) (↑ NUM) = pl - Ø (suffix for verbs) (↑ PERS) = 3 (↑ VFORM) = fin (↑ SUBJ NUM) = pl f5 f4

55. Lions seem to live in the forest DET N P NP V PP COMP VP f4 N f5 V VP-bar NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + lion: N seem: V (f4 PRED) = `lion’ (f5 PRED) = ‘seem SUBJ’ XCOMP (f5 SUBJ) = (f5 XCOMP SUBJ) -s (suffix for nouns) (f4 NUM) = pl - Ø (suffix for verbs) (f4 PERS) = 3 (f5 VFORM) = fin (f5 SUBJ NUM) = pl f5 f4

56. What is an XCOMP A non-finite clause, predicate nominal, predicate adjective, or predicate PP –Sam seemed to be happy (VP) –Sam seemed happy (AP) –Sam became a teacher (NP) –We had them arrested (VP) –We kept them in the drawer (PP) Has to be an argument of a verb: –Arrested by the police, Sam had no alternative but to give up his life of crime. This is an adjunct, not an XCOMP Gets its subject by sharing with another verb: –I think that Sam is happy. This is a COMP, not an XCOMP

57. Lions seem to live in the forest DET N P NP f7V PP f6COMP VP f9 N f5 V f8 VP-bar NP VP f3 S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + seem: V (↑ PRED) = ‘seem SUBJ’ XCOMP ( ↑ SUBJ) = ( ↑ XCOMP SUBJ) ( ↑ XCOMP VFORM) = INF - Ø (suffix for verbs) ( ↑ VFORM) = fin ( ↑ SUBJ NUM) = pl to: COMP - Ø (suffix for verbs) ( ↑ VFORM) = INF live: V ( ↑ PRED) = `live ’ SUBJ OBL VP → V VP ↑=↓ (↑ XCOMP) = ↓ f3 f5 f9 f8 f7 f6

58. Lions seem to live in the forest DET N P NP f7V PP f6COMP VP f9 N f5 V f8 VP-bar NP VP f3 S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + seem: V (f5 PRED) = ‘seem SUBJ’ XCOMP (f5 SUBJ) = (f5 XCOMP SUBJ) (f5 XCOMP VFORM) = INF - Ø (suffix for verbs) (f5 VFORM) = fin (f5 SUBJ NUM) = pl to: COMP - Ø (suffix for verbs) (f6 VFORM) = INF (f7 VFORM) = INF live: V (f7 PRED) = `live ’ SUBJ OBL VP → V VP f3=f5 (f3 XCOMP) = f8 f3 f5 f9 f8 f7 f6

59. Lions try to live in the forest DET N P NP V PP COMP VP N V VP-bar NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘try ’ SUBJ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF +

60. Lions have lived in the forest DET N P NP V PP VP N V NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘have SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM PASTPART PRED ‘live ’ SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + have: V (↑ PRED) = ‘have SUBJ’ XCOMP ( ↑ SUBJ) = ( ↑ XCOMP SUBJ) ( ↑ XCOMP VFORM) = PASTPART - Ø (suffix for verbs) ( ↑ VFORM) = fin ( ↑ SUBJ NUM) = pl

61. Lions were hunted in the forest DET N P NP V PP VP N V NP VP S SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘be SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM PASSIVE PRED ‘hunt ’ Ø SUBJ OBL-loc OBJ OBL -loc CASE OBL-loc PRED ‘in ’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + were : V (↑ PRED) = ‘be SUBJ’ XCOMP ( ↑ SUBJ) = ( ↑ XCOMP SUBJ) ( ↑ XCOMP VFORM) = PASSIVE ( ↑ VFORM) = fin ( ↑ SUBJ NUM) = pl