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Lexical Functional Grammar

Lexical Functional Grammar. History: Joan Bresnan (linguist, MIT and Stanford) Ron Kaplan (computational psycholinguist, Xerox PARC) Around 1978. What is Linguistic Theory. Delimit the range of possible human languages. What do all languages have in common?

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Lexical Functional Grammar

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

  2. 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.

  3. 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.

  4. 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

  5. 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.

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

  7. 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.”

  8. 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

  9. 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

  10. 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)

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

  12. 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

  13. 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.

  14. 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

  15. A surprise • 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

  16. Surprise (continued) • 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.

  17. 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.

  18. 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

  19. 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!!!

  20. 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.

  21. 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.

  22. 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!!!

  23. 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.

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

  25. 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 -

  26. 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’

  27. 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.

  28. 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

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

  30. 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

  31. 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

  32. 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.

  33. 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

  34. 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.”

  35. 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

  36. 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

  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 + 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

  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

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