October 25, 2006

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. S VP’ NP VP Some differences between English and Warlpiri Aux V NP The two small children are chasing that dog. S NP AUX V NP NP NP 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

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.ABSPRES-REFL hit-NONPAST “Herself is hitting Napaljarri.”

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

10. S VP’ NP VP Aux V NP What English and Warlpiri have in common according to Chomsky Deep structure Warlpiri Surface Structure 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. VP VP V PP V NP OBL OBJ Levels of Representation in LFG [s [np The bear] [vp ate [np a sandwich]]] constituent structure Grammatical encoding SUBJ PRED OBJ functional structure Lexical mapping Agent eat patient thematic roles Eat < agent patient > lexical mapping SUBJ OBJ Grammatical Encoding For English!!! S NP SUBJ

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

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

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< agent patient > 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<subj ob>’, ‘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 squared • 1 • 4 • 9 • 16 • 25 Each feature has a unique value. Also, another term for grammtical relation is grammatical function. features values

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

31. Give a name to each function f1 SUBJ PRED ‘bear’ NUM sg PERS 3 DEF + PRED ‘eat< agent patient > SUBJ OBJ TENSE past OBJ PRED ‘sandwich’ NUM sg PERS 3 DEF - 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. SUBJ PRED ‘lion’ NUM pl PERS 3 PRED ‘seem < theme > SUBJ’ XCOMP TENSE pres VFORM fin XCOMP SUBJ [ ] VFORM INF PRED ‘live< theme loc >’ SUBJ OBL-loc OBJ OBL-loc CASE OBL-loc PRED ‘in<OBJ>’ OBJ PRED ‘forest’ NUM sg PERS 3 DEF + S NP VP N V VP-bar COMP VP V PP P NP DET N Lions seem to live in the forest

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

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

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. The formalism for grammatical encoding :Local co-description of partial structures • 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” ()

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

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