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Corpus Approach vs. Generative Approach and Movement vs. Grammatical Functions One-Soon Her 何萬順 Corpus Approach vs. Generative Approach and Movement vs. Grammatical Functions One-Soon Her 何萬順

OUTLINE 1) Contrasting GA and CA 2) Contrasting LFG and TG 3) Conclusion OUTLINE 1) Contrasting GA and CA 2) Contrasting LFG and TG 3) Conclusion

1) Contrasting GA and CA What is the ultimate goal of a generative syntactic 1) Contrasting GA and CA What is the ultimate goal of a generative syntactic theory?

To account for the universal properties and variations in the syntactic phenomena in all To account for the universal properties and variations in the syntactic phenomena in all languages, in the simplest way.

A. 2 What is the ultimate goal of a corpus-based syntactic theory? A. 2 What is the ultimate goal of a corpus-based syntactic theory?

To discover generalizations and variations in the syntactic phenomena from the corpus materials at To discover generalizations and variations in the syntactic phenomena from the corpus materials at hand.

Let’s see a simple non-linguistic demonstration of CA vs. GA Let’s see a simple non-linguistic demonstration of CA vs. GA

Driving on Planet Earth Research Goal: to come up with a description of the Driving on Planet Earth Research Goal: to come up with a description of the side of the road to drive on, on Planet Earth

Corpus Approach “Look and see” Solution (1): Australia left China right Singapore left Taiwan Corpus Approach “Look and see” Solution (1): Australia left China right Singapore left Taiwan right USA right etc.

Evaluating Corpus Solution (1) Not happy Must make generalizations Evaluating Corpus Solution (1) Not happy Must make generalizations

Corpus Approach, Solution (2) Generalization: in some countries, drive on the left; in others, Corpus Approach, Solution (2) Generalization: in some countries, drive on the left; in others, drive on the right. Australia China Singapore Taiwan USA etc. left right

How about the generative approach? How about the generative approach?

The generative approach assumes: 1) there are universal principles 2) variation is due to The generative approach assumes: 1) there are universal principles 2) variation is due to parameters

Generative Approach Solution (1): Australia left China right Singapore left Taiwan right USA right Generative Approach Solution (1): Australia left China right Singapore left Taiwan right USA right etc.

Evaluating Solution (1) What’s the predictive power? Does it rule out the following? Country Evaluating Solution (1) What’s the predictive power? Does it rule out the following? Country X middle Country Y AM-left/PM-right Country Z Men-left/Women-right

Evaluating Solution (1) Each listing is a stipulation, thus no predictive power. Must generalize Evaluating Solution (1) Each listing is a stipulation, thus no predictive power. Must generalize and make predictions!

Generative Approach, Solution (2) Principle: within a country, drive on x side only. Parameter: Generative Approach, Solution (2) Principle: within a country, drive on x side only. Parameter: x = left/right Australia x = left China Singapore Taiwan USA etc. x = right x = left x = right

Evaluating Generative Solution (2) Pretty good, but…. 1) each listing still a stipulation 2) Evaluating Generative Solution (2) Pretty good, but…. 1) each listing still a stipulation 2) a parameter always a disjunction

Evaluating Generative Solution (2) Research question: can we get rid of the parameter and Evaluating Generative Solution (2) Research question: can we get rid of the parameter and the listings? The research is now theory-driven, rather than data-driven, as the data have been accounted for.

Evaluating Generative Solution (2) Expanding the scope of data: side of the road + Evaluating Generative Solution (2) Expanding the scope of data: side of the road + side of the driver

Driving on the Left Right Driving on the Left Right

Driving on the Right Left Driving on the Right Left

The driving side is always the opposite of the driver side!! The driving side is always the opposite of the driver side!!

Generative Approach, Solution (3) Principle: on Planet Earth, drive on the left, if the Generative Approach, Solution (3) Principle: on Planet Earth, drive on the left, if the driver seat is on the right; otherwise, drive on the right.

Evaluating Generative Solution (3) Wow, no listings and no parameters!! But, wait! There’s still Evaluating Generative Solution (3) Wow, no listings and no parameters!! But, wait! There’s still a disjunction.

Evaluating Generative Solution (3) Principle: on Planet Earth, if the driver seat is on Evaluating Generative Solution (3) Principle: on Planet Earth, if the driver seat is on the right, then drive on the left; otherwise, drive on the right.

Evaluating Generative Solution (3) Let’s again expand the scope of data: driver + passenger Evaluating Generative Solution (3) Let’s again expand the scope of data: driver + passenger + center of the road

Right Right

Left Left

The driver is always closer to the center of the road!! The driver is always closer to the center of the road!!

Generative Approach, Solution Ultimate Principle: when driving on Planet Earth, stay closer to the Generative Approach, Solution Ultimate Principle: when driving on Planet Earth, stay closer to the center of the road in relation to the front seat passenger.

Evaluating GG Solution Ultimate Does it allow a functional explanation? Yes, it does! Being Evaluating GG Solution Ultimate Does it allow a functional explanation? Yes, it does! Being closer to the center of the road affords the driver the best range of vision with the least physical strain

Evaluating GA Solution Ultimate It’s simple and elegant, but is it complete? Evaluating GA Solution Ultimate It’s simple and elegant, but is it complete?

Evaluating GG Solution Ultimate Consider 建國高架橋下迴轉道 US Postman’s jeep And, of course, Myanmar! Evaluating GG Solution Ultimate Consider 建國高架橋下迴轉道 US Postman’s jeep And, of course, Myanmar!

Evaluating GG Solution Ultimate …the two kinds of linguists need each other. Or better, Evaluating GG Solution Ultimate …the two kinds of linguists need each other. Or better, that the two kinds of linguists, wherever possible, should exist in the same body. (Fillmore 1992: 35)

Evaluating GG Solution Ultimate Lessons from Myanmar and Pirahã. Evaluating GG Solution Ultimate Lessons from Myanmar and Pirahã.

Evaluating GG Solution Ultimate It’s simple and elegant, but how many countries do you Evaluating GG Solution Ultimate It’s simple and elegant, but how many countries do you really need to observe to derive it?

2) Contrasting LFG and TG 1. 2. 3. 4. 5. Motivation Phrase structures Grammatical 2) Contrasting LFG and TG 1. 2. 3. 4. 5. Motivation Phrase structures Grammatical features Theta roles & linking Summary & examples

1. Motivation 1. Motivation

Under the Generative Grammar, there are many competing frameworks: TG (incl. GB, MP…) LFG Under the Generative Grammar, there are many competing frameworks: TG (incl. GB, MP…) LFG HPSG etc.

They share the same goal, but differ in: 1) what is “simple” exactly? 2) They share the same goal, but differ in: 1) what is “simple” exactly? 2) the right balance between descriptive adequacy and theoretical elegance Consequence: somewhat different architectures some different primitive notions

2. Phrase Structures a. k. a. c(onstituent)-structures 2. Phrase Structures a. k. a. c(onstituent)-structures

TG Principles: X-bar scheme for DS (spec rule) XP → YP, X’ (comp rule) TG Principles: X-bar scheme for DS (spec rule) XP → YP, X’ (comp rule) X’ → ZP, X Parameters: (spec rule) YP > X’ or X’ > YP (comp rule) ZP > X or X > ZP

Extremist View (Kayne 1994) : Universal X-bar scheme with fixed order: spec > head Extremist View (Kayne 1994) : Universal X-bar scheme with fixed order: spec > head > complement No PS parameters in DS!

TG DS → movements → SS TG DS → movements → SS

TG That, I don’t know t. John was kisses t. TG That, I don’t know t. John was kisses t.

LFG Single level c-structure Language-specific PSR allowed X-bar scheme as default LFG Single level c-structure Language-specific PSR allowed X-bar scheme as default

LFG That, I don’t know. John was kisses. No DS, no movements. WYSIWYG. LFG That, I don’t know. John was kisses. No DS, no movements. WYSIWYG.

3. Grammatical Features e. g. , case, number, person, etc. 3. Grammatical Features e. g. , case, number, person, etc.

TG Features grow on trees. Mary has [3/sg/nom] kissed John …. . TG Features grow on trees. Mary has [3/sg/nom] kissed John …. .

LFG Features & Grammatical Functions form an independent f-structure C-structure Mary has kissed John LFG Features & Grammatical Functions form an independent f-structure C-structure Mary has kissed John

4. Theta roles & linking 4. Theta roles & linking

TG Theta roles are assigned to tree positions. kiss [x y] Mary has kissed TG Theta roles are assigned to tree positions. kiss [x y] Mary has kissed John

LFG Theta roles, or argument roles, also form an independent a-structure, which is linked LFG Theta roles, or argument roles, also form an independent a-structure, which is linked with the predicate’s f-structure kiss

5. Summary & examples 5. Summary & examples

TG (1) John, Mary has kissed. kiss [x y] DS Mary has 3/sg/nom kissed TG (1) John, Mary has kissed. kiss [x y] DS Mary has 3/sg/nom kissed John …. .

TG (2) John, Mary has kissed. Movements John Mary has 3/sg/nom kissed t …. TG (2) John, Mary has kissed. Movements John Mary has 3/sg/nom kissed t …. .

TG (3) John, Mary has kissed. John Mary has [3/sg/nom] kissed t …. Feature TG (3) John, Mary has kissed. John Mary has [3/sg/nom] kissed t …. Feature checking …. .

TG (4) John, Mary has kissed. SS John Mary has kissed t TG (4) John, Mary has kissed. SS John Mary has kissed t

LFG (1) John, Mary has kissed. c-structure John …. Mary has kissed …. LFG (1) John, Mary has kissed. c-structure John …. Mary has kissed ….

LFG (2) John, Mary has kissed. kiss <x y> f-structure LFG (2) John, Mary has kissed. kiss f-structure

TG vs. LFG In a nutshell (1) TG LFG Movements Yes No Grammatical functions TG vs. LFG In a nutshell (1) TG LFG Movements Yes No Grammatical functions No Yes

TG vs. LFG In a nutshell (2) TG: tree-centric theta roles and grammatical features TG vs. LFG In a nutshell (2) TG: tree-centric theta roles and grammatical features are all part of the tree LFG: parallel planes argument structure, functional structure, and constituent structure all independent

An Overview of LFG 1. 2. 3. 4. 5. Lexical entries Phrase structure rules An Overview of LFG 1. 2. 3. 4. 5. Lexical entries Phrase structure rules C-structure F-structure Correspondence between c- and f-structure

1. Sample lexical entries time N flies V 1. Sample lexical entries time N flies V

2. Sample phrase structure rules S → NP: SUBJ VP VP → V NP: 2. Sample phrase structure rules S → NP: SUBJ VP VP → V NP: OBJ NP → N

3. Sample c-structure S NP: SUBJ VP N V time flies 3. Sample c-structure S NP: SUBJ VP N V time flies

4. Sample f-structure S NP: SUBJ VP N V time flies 4. Sample f-structure S NP: SUBJ VP N V time flies

5. Correspondence between c- and f-structure S NP: SUBJ VP N V time flies 5. Correspondence between c- and f-structure S NP: SUBJ VP N V time flies

Some of LFG’s Motivations 1. 2. 3. 4. Lexical integrity Non-configurationality Movement paradoxes Lexical Some of LFG’s Motivations 1. 2. 3. 4. Lexical integrity Non-configurationality Movement paradoxes Lexical processes over movements

1. Lexical Integrity Hypothesis (Huang 1984) No phrase-level rule may affect a proper subpart 1. Lexical Integrity Hypothesis (Huang 1984) No phrase-level rule may affect a proper subpart of a word. Ex: I like singing and dancing → *I like [sing and dance]-ing. You speak and I do too. → *He is a singer and I do too.

TG Mary went. Mary /ed/ go Affix Hopping Violating lexical integrity. TG Mary went. Mary /ed/ go Affix Hopping Violating lexical integrity.

LFG Mary went Maintaining lexical integrity. LFG Mary went Maintaining lexical integrity.

2. Non-configurationality English is a configurational language, where grammatical relations (e. g. , SUBJ, 2. Non-configurationality English is a configurational language, where grammatical relations (e. g. , SUBJ, OBJ) are largely encoded by the configuration of the constituent structure. There are, however, non-configurational languages, where grammatical relations are largely encoded by morphological means.

Language Typology 101 V i: V t: → Nominative (unmarked) S A P S Language Typology 101 V i: V t: → Nominative (unmarked) S A P S A → Accusative language → Absolutive (unmarked) P Ergative language Case can be marked structurally or morphologically!

English Subj Obj Mary has kissed John has kissed Mary Case marked by structural English Subj Obj Mary has kissed John has kissed Mary Case marked by structural configuration.

Yes, I speak Malayalam Case marked by affixes. Yes, I speak Malayalam Case marked by affixes.

Malayalam 1. Kutti ψ aana-ye kantu child. NOM elephant-ACC saw 2. kutti kantu aana-ye Malayalam 1. Kutti ψ aana-ye kantu child. NOM elephant-ACC saw 2. kutti kantu aana-ye 3. aana-ye kutti kantu 4. aana-ye kantu kutti 5. kantu kutti aana-ye 6. kantu aana-ye kutti Case marked by affixes. (SOV) (SVO) (OSV) (OVS) (VSO) (VOS)

Malayalam (TG) (fixed DS, fixed order) kutti (LFG) kutti aana-ye kantu (lots of movements!) Malayalam (TG) (fixed DS, fixed order) kutti (LFG) kutti aana-ye kantu (lots of movements!) (no DS, no ordering) aana-ye kantu (no movements!) Which is simpler?

Malayalam F-structure for all six word orders Malayalam F-structure for all six word orders

Warlpiri The two small children are chasing that dog. wita-jarra-rlu kurdu-jarra- rlu small-DUAL-ERG child-DUAL-ERG Warlpiri The two small children are chasing that dog. wita-jarra-rlu kurdu-jarra- rlu small-DUAL-ERG child-DUAL-ERG ka-pala pres-3 du. SUBJ wajili-pi-nyi chase-NPAST yalumpuψ that. ABS malikiψ dog. ABS

Warlpiri Word order: Free Constraints: 1) 1 st position must be a constituent 2) Warlpiri Word order: Free Constraints: 1) 1 st position must be a constituent 2) 2 nd position must be T (AUX) Examples: 1) [that. ABS dog. ABS]NP T chase children-ERG small-ERG 2) [dog. ABS]N T children-ERG chase small-ERG that. ABS 3) [chase]V T children-ERG dog. ABS small-ERG that. ABS 4) *[T]T chase small-ERG children-ERG that. ABS dog. ABS 5) *[small-ERG dog. ABS]*C T chase children-ERG that. ABS

Warlpiri TG (same as English) NP T VP Consequence: lots of movements Prediction: Warlpiri, Warlpiri TG (same as English) NP T VP Consequence: lots of movements Prediction: Warlpiri, like Eng, has VP Test: *[chase dog. ABS]VP T children-ERG Result: Warlpiri has no VP!

Warlpiri LFG TP → C T C* C T C. . . Typology: X-bar Warlpiri LFG TP → C T C* C T C. . . Typology: X-bar vs. W-star Cause: morphology competes with syntax

3. Movement paradoxes 1. a. *The theory does explain. b. The theory does explain 3. Movement paradoxes 1. a. *The theory does explain. b. The theory does explain that mass is energy. c. That mass is energy, theory does explain t. 2. a. *The theory does capture. b. *The theory does capture that mass is energy. c. That mass is energy, theory does explain t.

3. Movement paradoxes 1. a. You are not a student. b. Are you not 3. Movement paradoxes 1. a. You are not a student. b. Are you not a student? c. You aren’t a student. d. Aren’t you a student? 2. a. I am not a student. b. Am I not a student? c. *I aren’t a student. d. Aren’t I a student?

3. Movement paradoxes 1. a. *他最擅長. b. 他最擅長語言學. c. 語言學,他最擅長 t. 2. a. *他最拿手. 3. Movement paradoxes 1. a. *他最擅長. b. 他最擅長語言學. c. 語言學,他最擅長 t. 2. a. *他最拿手. b. *他最拿手語言學. c. 語言學,他最拿手 t.

3. Movement paradoxes TG: mismatches are unexpected, because the source and the target of 3. Movement paradoxes TG: mismatches are unexpected, because the source and the target of movement must be identical. LFG: mismatches are expected, because there is no movement and mapping between two planes (e. g. , c- and fstructure) is not one-to-one.

4. Lexical processes over movements Participle verbs (present, perfect, passive) in English may convert 4. Lexical processes over movements Participle verbs (present, perfect, passive) in English may convert to adjectives. 1. a very disturbed market. (passive) 2. a well-prepared student. (perfect) 3. an all smiling bride. (present) Particle V → A

4. Lexical processes over movements happy [x] TG was happy John Prediction: V[x] → 4. Lexical processes over movements happy [x] TG was happy John Prediction: V[x] → A[x], x undergoes movement

4. Lexical processes over movements True for passive and unaccusative verbs disturbed [x y] 4. Lexical processes over movements True for passive and unaccusative verbs disturbed [x y] TG was disturbed the market

4. Lexical processes over movements Not true for unergative verbs prepared [x] TG John 4. Lexical processes over movements Not true for unergative verbs prepared [x] TG John has prepared well V[x] → A[x], x undergoes no movement

4. Lexical processes over movements happy <x> LFG <SUBJ> John was happy Prediction: V[x] 4. Lexical processes over movements happy LFG John was happy Prediction: V[x] → A[x]

4. Lexical processes over movements True for all intransitive participle verbs. disturbed <x y> 4. Lexical processes over movements True for all intransitive participle verbs. disturbed LFG The market was disturbed

3) CONCLUSION The air-mattress metaphor 3) CONCLUSION The air-mattress metaphor

Corpus Approach vs. Generative Approach and Movement vs. Grammatical Functions One-Soon Her 何萬順 Corpus Approach vs. Generative Approach and Movement vs. Grammatical Functions One-Soon Her 何萬順