The Morphosyntax of Upward Agreement and Downward Agreement

Introduction, Part IV: Differences between Upward Agreement and

Downward Agreement

Anke Himmelreich anke.assmann@uni-leipzig.de

Universität Leipzig, Institut für Linguistik

16 November, 2017

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Table of Contents

1 Summary of Part III

2 Cases of Agreement

3 Possibilities for Analyzing Instances of Agreement

4 Choosing between the Possibilities

5 Summary of Part IV

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Table of Contents

1 Summary of Part III

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Conceptual Aspects

Conceptual Aspects = Complexity Complexity = running time / memory space Running time: Number of basic steps depending on the size of the input. Memory space: Maximal memory space consumption in the time of the application of the algorithm depending on the size of the input.

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Conceptual Aspects

Conceptual Aspects = Complexity Complexity = running time / memory space Running time: Number of basic steps depending on the size of the input. Memory space: Maximal memory space consumption in the time of the application of the algorithm depending on the size of the input.

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Linguistic Theories as Algorithms

Problem How do we get a specific linguistic expression?

Algorithm: 1. Apply rule Ti to get representation r1. 2. Apply rule Tj to get representation r2. 3. Apply rule Tk to get representation r3.

... n. Apply rule Tz to get representation rn.

Input: A representation, i.e., a structure, (that is evaluated or transformed), the size of which might be the number of elements in the representation or the number of relations (ordered pairs/n-tuples of elements) Basic step: A minimal transformation/operation Memory space: Number of elements in the representation that one needs to look at at one point in the algorithm

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Direction Condition as an Algorithm

C-command as an algorithm C-command relations between X and Y can be read off of a syntactic tree. If trees are displays of a derivation and derivations are algorithms, then c-command can be understood as the part of the derivation that have got X and Y into there positions.

Problem Finding out if two nodes X and Y in a tree are in a c-command relation. (C-command (X,Y)?) Finding a node Y that is in a c-command with a given node X. (Search-Goal (X))

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Worst Case Complexity and Big O

Worst-case complexity tells us something about the maximum of resources we should plan in. When it comes to comparing (worst-case) complexity, it is more interesting to identify to which complexity group an algorithm belongs than to simply compare the complexity, because the goal is to classify algorithms. The Big O notation is the notation for the growth rate (or order ) of a function, i.e., it says something about how much complexity increases if the size of the input increases. Since the size of the input approaches infinity, concrete constants and coefficients can be neglected.

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Os and the Direction Condition

The most interesting part of Agree, when it comes to growth orders, is determining the goal. The worst complexity has an Agree algorithm that uses sequential search (O(n)). This is independent of whether the tree is (primarily) searched upwards or downwards.

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So what?

Assuming that complexity is the main design aspect under which a theory is evaluated, there is no general preference of one Agree direction over the other. Thus, only empirical arguments do really matter.

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Table of Contents

2 Cases of Agreement Cases of Downward Agreement Cases of Upward Agreement Cases of Bidirectional Agreement Comparison of the Cases

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Table of Contents

2 Cases of Agreement Cases of Downward Agreement Cases of Upward Agreement Cases of Bidirectional Agreement Comparison of the Cases

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φ-Agreement

Hindi (Bhatt (2005, 775), Boeckx (2004, 26))

(1) a. Vivek-ne Vivek-ERG

kitaab book.F.SG

parh-nii read-INF.F.SG

chaah-ii. want-PERF.F.SG

‘Vivek wanted to read the book.’ b. Mona

Mona kuttõ-ko dog.M.PL-ACC

dekh-naa/*nii see-INF/*INF.F.SG

chaah-tii want-HAB.F.SG

thii. be-PAST.F.SG ‘Mona wanted to see the dogs.’

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Table of Contents

2 Cases of Agreement Cases of Downward Agreement Cases of Upward Agreement Cases of Bidirectional Agreement Comparison of the Cases

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Negative Concord

(2) a. ?John didn’t eat nothing. b.??Nobody didn’t eat.

Blanchette (2016), Zeijlstra (2004)

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Tense Concord

(3) a. John said Mary was ill b. Jan

John zei said

dat that

Marie Mary

ziek ill

was was

‘John said Mary was ill’

Zeijlstra (2012)

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Binding and Co-Reference

(4) a. She likes herself. b. He likes himself. c. She likes her. d. You like yourself. e. They like themselves..

(5) A: I met the most fascinating woman yesterday. B: Oh yeah? Who was she/*he?

Preminger and Polinsky (2015)

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Table of Contents

2 Cases of Agreement Cases of Downward Agreement Cases of Upward Agreement Cases of Bidirectional Agreement Comparison of the Cases

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Himmelreich (2017b)

Phenomenon?

How does Agree work?

What is the probe?

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Assmann et al. (2015)

Phenomenon?

How does Agree work?

What is the probe?

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φ-Agreement with Vs, Ns, As is Agree I

Agreement Asymmetries between verbs, nouns, and adjectives (Swahili, (Baker, 2008, 1f))

(6) a. Ni-li-kuwa 1SS-PAST-be

ni-ki-som-a. 1SS-CONT-read-FV

‘I was reading.’ b. Ni-∅

1SS-be m-refu. CL1-tall

‘I am tall.’ c. Ni-li-po-kuwa

1SS-PAST-when-be ki-jana CL7-child

... now

sasa 1SS-be-when

ni-li-po CL1-man

m-tu CL1-whole

m-zima, ...

‘When I was a child ... Now that I am a man ...’

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φ-Agreement with Vs, Ns, As is Agree II

Baker (2008, ch.2): Universal bidirectional Agree at first glance

(7) A functional head F agrees with XP, XP a maximal projection, only if: a. F c-commands XP or XP c-commands F b. There is no YP such that F c-commands YP, YP

c-commands XP, and YP has φ-features c. F and XP are contained in all the same phases (e.g., full

CPs) d. XP is made active for agreement by having an unchecked

case feature

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φ-Agreement with Vs, Ns, As is Agree III

(8) a. Verbs are lexical categories that license a specifier. b. Nouns are lexical categories that have a referential index. c. Adjectives are lexical categories that have neither a

specifier nor a referential index.

(9) Any lexical category can be immediately dominated by the projection of a functional head that matches it in gross categorical features. Functional heads, unlike lexical heads, can manifest agreement.

(10) The Structural Condition on Person Agreement (SCOPA) A functional category F can bear the features +1 or +2 if and only if a projection of F merges with an NP that has that feature, and F is taken as the label for the resulting phrase.

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Parametrization I

(Baker, 2008, 215)

(11) The Direction of Agreement Parameter a. F agrees with DP/NP only if DP/NP asymmetrically

c-commands F, or b. F agrees with DP/NP only if F c-commands DP/NP, or c. F agrees with DP/NP only if F c-commands DP/NP or vice

versa.

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Parametrization II

(12) a. On the table were/*was (put) some peanuts. b. On the table was/*were (put) a peanut.

(Kinande, Baker (2003))

(13) a. Omo-mulongo LOC.18-village.3

mw-a-hik-a 18S-T-arrive-FV

mukali. woman.1

‘At the village arrived a woman.’ b. Oko-mesa

LOC.17-table kw-a-hir-aw-a 17S-T-put-pass-FV

ehilanga. peanuts.19

‘On the table were put peanuts.’

(Burushaski, Willson (1996, 3))

(14) a. Dası́n girl(ABS)

há-e house-OBL

le in

mó-yan-umo. 3SO.F-sleep-3SS.F/PAST

‘The girl slept in the house.’ b. Dası́n

girl(ABS) há-e house-OBL

le in

huruT-umo. sit-3SS.F/PAST

‘The girl sat in the house.’

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Table of Contents

2 Cases of Agreement Cases of Downward Agreement Cases of Upward Agreement Cases of Bidirectional Agreement Comparison of the Cases

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Possible Differences between Upward and Downward Agree

Type of Feature Downward Agree Upward Agree φ case

tense negation indices(?)

Locality Downward Agree Upward Agree

within phrase (spec-head) within clause within clause long-distance long-distance

Categories of Probe and Goal Downward Agree Upward Agree verbal verbal

nominal

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Table of Contents

3 Possibilities for Analyzing Instances of Agreement Interaction of Movement and Agreement Indirect Agreement No Agr