Topic Structures of Mandarin Chinese in Dynamic Syntax

152900093 Dynamic Syntax Module, Assignment 2

School of Oriental & African Studies, 2016

This essay is a description of topic structures in Mandarin Chinese using the tools of Dynamic

Syntax. First, this essay will set out the nature of parsing in DS, followed by the logic of finite

trees (LOFT). Then some time will be taken to analyze the topic structure of Mandarin Chinese

in comparison to English. Paying keen attention to the topic first aspect of Chinese grammar.

After that we will flesh out the topic structures of Chinese in depth using the tools DS has given

us. By Chinese what I mean is the standardised national language of PR China, Taiwan and

Singapore.

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1.0 Introduction

1.1 The Nature of DS

1.2 Logic of Finite Trees (LOFT)

1.3 Topic Structures

2.0 Main Analysis

3.0 Summary

4.1 Appendix

4.2 Bibliography

1.0 Introduction

1.1 The Nature of DS

What is Dynamic Syntax? What is it used for? These are big questions we must ask ourselves if

we are to move on further in this study. The nature of a dynamic type of syntax is to deal with

partial information in a step-by-step process, very much the same way humans do on a day-to-

day basis. Another term for this used often in syntax is the notion of ‘parsing’, the action of

dealing with the information as it presents itself. So if we take a sentence like Hilary upset Joan

the process that we will be modelling as DS will look at the structure of the sentence word-by-

word as a left-to-right process. How DS does this is it takes the familiar notion of TREE

STRUCTURE and applies it to represent not the structure of words in a string but the

interpretations of words uttered in a given context (Cann et al. 2005:31). We can see this by the

structure given below in (1).

(1) (Upset’ (Joan’))(Hilary’)

Hilary’ Upset’ (Joan’)

Joan’ Upset’

What is most important about the structure we get in Figure 1 is the fact that no information is

given about word order in English. English could be verb final, verb initial or something else for

all we know. This is what gives DS its edge over other syntactic theories out there like LFG.

Each node is described as a concept, which is compositionally derived. The tree thus reflects a

jigsaw view of complex concepts, notable not a jigsaw view of words (Cann et al. 2005:32).

Tree structures in DS are not representations of the structure of sentences, like in other syntactic

theories. They are instead structured representations of interpretation assigned to sentences,

where the steps on the way to the final output are as important as the final output itself. Through

DS we can derive underlying forms such as TOPIC and FOCUS through the process of tree

construction, instead of just assigning them to some other layer of information, because they are

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too problematic to deal with. The forms we deal with in DS are strictly time linear, as we

represent the parse of sentences, extracting information from a string in context. We do this by

assuming the progressive states like those in (2) seen below.

(2) ?Ty(t) ?Ty(t)

Hilary’

?Ty(t) Ty(t)

Hilary’ ● Hilary’ ●

● Upset’ Upset’ Joan’

?Ty(t) (Upset’ (Joan’)(Hilary’))

Hilary’ Upset’ (Joan’) Hilary’ Upset’ (Joan’)

Joan’ Upset’ Joan’ Upset’

1.2 Logic of Finite Trees (LOFT)

Now it is important to take a quick glance at the logic of finite trees (LOFT) in order to

understand how tree structures work on a logical level. Modal logic describes tree structures as

binary branching in nature. That means there is only ever two daughters maximum for every

mother node on a tree. Each node is identified by a numerical order, number 0 to 1. The left

daughter is assigned n0, this is known as the argument daughter. The right daughter is assigned

n1, this is known as the functor. This is demonstrated in the following (3);

(3) 0

00 01

010 011

0110 0111

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The information gathered at each node forms a part of a Declarative Unit and is expressed by the

predicate Tn which stands for ‘tree node’. This can be seen in (4) representing the tree-locations

for the sentence John sings (Cann et al. 2005:38-39);

(4) Tn(0), Ty(t), Fo (Sing’(John’))

Tn(00), Ty(e), Tn(01), Ty(et),

Fo(John’) Fo(Sing’)

In this tree the left daughter becomes decorated with the argument formula, and the right

daughter gets decorated with the formula that applies to the functor, which in turn yields the

decoration at the mother node.

We can now describe the two basic modalities using arrows to symbolise the DAUGHTER

RELATION, with a “down” arrow <↓>, and the MOTHER RELATION, with an “up” arrow

<↑>.We can also add a numerical subscript in order to describe the functor daughter <↓1>, while

<↓0> refers to the argument daughter, as well as the following mother relations <↑1>, <↑0>, and

in this case there is only ever a single mother. As we use these symbols we can describe more

complex expressions like <↓0>Ty(e) at node n, which means “there exists an argument daughter

that node n immediately dominates which is decorated by the label Ty(e)”.

We can now use these basic modalities to describe every node on a tree, like (5). This provides a

means of identifying one node in the tree that some property holds of some other node.

(5) Tn(0), Q

Tn(00), P Tn(01), P Q

Tn(010), R Tn(011), R (P Q)

It is thus possible to describe the whole tree from any mode within it. Therefore the statements in

(6) are all true of this tree from the node of this tree decorated by R, the node with the treenode

address 010.

(6) a. <↑0> P Q holds

at my mother, P Q holds

b. <↑0><↓1> R (P Q)

at my mother’s functor daughter, R (P Q) holds

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c. <↑0><↑1> Q

at my mother’s mother, Q holds

d. <↑0><↑1><↓0> P

at my mother’s mother’s argument daughter, P holds

1.3 Topic Structures

What we will now do is discuss what topic structures are in general, taking a quick look at

English structures and then focusing our effort on Mandarin structures. Kroeger describes both

the TOPIC and the FOCUS as pragmatic functions which are elements the sentence can bear. To

put it in layman’s terms the topic is broadly “what the sentence is about” (Li & Thompson

1989:85), (Kroeger 2004:136). We can use English to draw our first example in (7), looking at

several topic and comment constructions, the COMMENT being the remainder of the sentence

(Kroeger 2004:137). The topics are underlined as so,

(7) Contrastive Topic:

This ice cream I like, (but the stuff we had yesterday was awful).

Left-Dislocation:

My friend John, a snake bit him on the hand and he lost three fingers.

External Topic:

As for Manila, the traffic is unbelievable.

Here, in (7), it is quite obvious that the information supplied in these three different examples has

been fronted. It has been taken out from the remainder of the information given in the sentence,

the comment, and put to the beginning of the sentence so the hearer recognises what the speaker

wants the theme on the sentence to be about. In some languages there are markers which are

tagged onto words, usually at the end as a suffix, in order to let the hearer know what is the topic.

Korean and Japanese are good examples which use the topic suffixes neun/eun and ha/wa

respectively. Mandarin is what is known as a topic-prominent language, this is discussed quite

extensively in Li & Thompson (1989). What Mandarin does is uses its word order to mark the

topic always as the first part of the sentence uttered. It does seem similar to English but with

some marked differences. Take the following example in (8), EXP stands for experiential aspect

and CRS for Current Relevant State, which is explained in (Li & Thompson 1989:296).

(8) gou wo kan - guo le

dog I see - EXP CRS

The dog/Dogs (generic) I have already seen.

What is important to note is the fact that the first part of the sentence, what is underlined in (8), is

always the topic. If instead we said wo kan-guo le gou, which is a perfectly grammatical Chinese

sentence, then gou would no longer be topic, it would instead be wo. That is an example of a

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sentence where the topic and the subject are in fact exactly the same. A good way to think of the

subject is that it has a “doing” relationship to the verb. Another good example is sentence (9)

below, taken from (Li & Thompson 1989:87).

(9) wo xihuan chi pingguo

I like eat apple

I like to eat apples.

What we can see from this sentence is that I is both the topic, what the sentence is about, and the

entity that has a doing relationship to the verbs of liking and eating.

2.0 Main Analysis

We have now come to the point where we are able to flesh out our main analysis of topic

structures in Mandarin Chinese, using the tools that DS has given us. We will turn to the

sentence in (8) first to conduct our analysis. For any of the rules mentioned below please see the

appendix.

(10) a. AXIOM

Tn(0), ?Ty(t), ◊

b. *ADJUNCTION

Tn(0), ?Ty(t)

<↑*>Tn(0), Ty(e), ?∃x.Tn(x), ◊

c. Parsing Gou

Tn(0), ?Ty(t)

<↑*>Tn(0), Ty(e), Fo(Gou’), ?∃x.Tn(x), ◊

d. INTRODUCTION and PREDICTION of subject and predicate

Tn(0), ?Ty(t)

Fo(Gou’), Ty(e), Tn(00), ?Ty(e), ◊ Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x)

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e. Parsing Gou wo

Tn(0), ?Ty(t)

Fo(Gou’), Ty(e), Tn(00), ?Ty(e), Tn(01), ?Ty(et), ◊

<↑*>Tn(0), ?∃x.Tn(x) Fo(Wo’)

f. Parsing Gou wo kan-guo

Tn(0), ?Ty(t), ASP(EXP)

Fo(Gou’), Ty(e), Tn(00), ?Ty(e), Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x) Fo(Wo’)

Tn(010), Ty(e(et)),

?Ty(e), ◊ Tn(011), Fo(Kanguo’)

g. Parsing Gou wo kan-guo le

Tn(0), ?Ty(t), ASP(EXP, CRS), ◊

Fo(Gou’), Ty(e), Tn(00), ?Ty(e), Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x) Fo(Wo’)

Tn(010), Ty(e(et)),

?Ty(e) Tn(011), Fo(Kanguo’)

h. The MERGE process

Tn(0), ?Ty(t), ASP(EXP, CRS)

Fo(Gou’), Ty(e), Tn(00), ?Ty(e), Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x) Fo(Wo’)

Tn(010), Ty(e(et)),

?Ty(e), ◊ Tn(011), Fo(Kanguo’)

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i. The result of MERGE

Tn(0), ?Ty(t), ASP(EXP, CRS)

Tn(00), ?Ty(e), Tn(01), ?Ty(et)

Fo(Wo’)

Tn(010), Ty(e) Ty(e(et)),

Fo(Gou’), ◊ Tn(011), Fo(Kanguo’)

We begin with the rules of Adjunction; this is how we introduce topics in the sentence. Cann et

al. (2005) goes into depth on how this is part of a strategy in dealing with left dislocation

structures. This is followed by the parsing of the topic, in this case the noun gou. We introduce

this through the following lexical input.

/kou˨˩˦/

IF Ty(e)

THEN put(Fo(Gou’), Ty(e)

ELSE Abort

After the parsing of the topic, we are now ready to introduce more nodes through the steps of

Introduction and Prediction. Then we parse the word wo once the new argument node has been

acquired. This is done through the following lexical input.

/wo˨˩˦/

IF ?Ty(e)

THEN put(Fo(Wo’), Ty(e)

ELSE Abort

After parsing the subject of the sentence, we are ready to parse the verb. This is the following

word kan-guo, made up of the verb ‘to see/watch’ and the experiential aspectual marker. Then

verb is parsed through the following lexical input.

/khæn˥˩kuo˩ /

IF ?Ty(et)

THEN go(<↑1>), put(ASP(EXP),

go(<↓1>), make(<↓1>), go(<↓1>),

put(Fo(Kanguo’), Ty(e(et)),

go(<↑1>), make(<↓0>), go(<↓0>), put(?Ty(e))

ELSE Abort

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Chinese verbs do not carry tense like English verbs, tense is inferred from the sentence as a

whole as through the addition of aspectual markers. This is why we are parsing aspectual

markers, all Chinese words are monosyllabic in nature, the combine together to form new words,

like kan-guo. This is why the following word that is parsed only adds another level of aspect to

the verb, this being the current relevant state (CRS). This is through the parse of the word le,

there are other forms of le, some ad perfective aspect, but we will not go into a detail analysis of

le here. Le is parsed through the following lexical input.

/lɤ/

IF ?Ty(t)

THEN put(ASP(CRS)

ELSE Abort

After all the words in the sentence string have been parsed, we are ready for the step of the

Merge process, seen in steps h. and the result in step i. In the result we see that the topic word

gou moves to the object position in the parse, in Chinese the sentence wo kanguo gou is a

perfectively acceptable sentence, and this is basically what the sentence is doing through the

result of Merge. What would be interesting to see is when the subject and the topic words are

identical, this happens a lot in Chinese, like in sentence (9) above. We analyse (9) through the

tools of DS in (11) below.

(11) a. AXIOM

Tn(0), ?Ty(t), ◊

b. *ADJUNCTION

Tn(0), ?Ty(t)

<↑*>Tn(0), Ty(e), ?∃x.Tn(x), ◊

c. Parsing Wo

Tn(0), ?Ty(t)

<↑*>Tn(0), Ty(e), Fo(Wo’), ?∃x.Tn(x), ◊

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d. INTRODUCTION and PREDICTION of subject and predicate

Tn(0), ?Ty(t)

Fo(Wo’), Ty(e), Tn(00), ?Ty(e), ◊ Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x)

e. Parsing Wo xihuan

Tn(0), ?Ty(t)

Fo(Wo’), Ty(e), Tn(00), ?Ty(e) Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x)

Tn(010), Ty(t), ◊ Tn(011), Ty(t(et)),

Fo(Xihuan’)

f. Parsing Wo xihuan chi

Tn(0), ?Ty(t)

Fo(Wo’), Ty(e), Tn(00), ?Ty(e) Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x)

Tn(010), Ty(t) Tn(011), Ty(t(et)),

Fo(Xihuan’)

Tn(0100), Ty (e) Tn(0101), Ty (et)

Tn(01010), Ty(e), ◊ Tn(01011),

Ty(e(et)), Fo(Chi’)

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g. Parsing Wo xihuan chi pingguo

Tn(0), ?Ty(t)

Fo(Wo’), Ty(e), Tn(00), ?Ty(e) Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x)

Tn(010), Ty(t) Tn(011), Ty(t(et)),

Fo(Xihuan’)

Tn(0100), Ty (e) Tn(0101), Ty (et)

Tn(01010), Ty(e), Tn(01011),

Fo(Pingguo’), ◊ Ty(e(et)), Fo(Chi’)

h. Double MERGE process

Tn(0), ?Ty(t)

Fo(Wo’), Ty(e), Tn(00), ?Ty(e) Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x)

Tn(010), Ty(t) Tn(011), Ty(t(et)),

Fo(Xihuan’)

Tn(0100), Ty (e) Tn(0101), Ty (et)

Tn(01010), Ty(e), Tn(01011),

Fo(Pingguo’), ◊ Ty(e(et)), Fo(Chi’)

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i. Result of Double MERGE process

Tn(0), ?Ty(t)

Tn(00), ?Ty(e), Tn(01), ?Ty(et)

Fo(Wo’)

Tn(010), Ty(t) Tn(011), Ty(t(et)),

Fo(Xihuan’)

Tn(0100), Ty (e), Tn(0101), Ty (et)

Fo(Wo’)

Tn(01010), Ty(e), Tn(01011),

Fo(Pingguo’) Ty(e(et)), Fo(Chi’)

What is interesting to see in the parsing of (11) is the fact that the topic and subject are identical.

The first verb to be passed xihuan acts as what Cann et al. (2005:36) refers to as a Proposition

Predicate. It is given below in the following lexical input.

/ɕi˨˩˦xuæn˦/

IF ?Ty(et)

THEN make(<↓1>), go(<↓1>),

put(Ty(t(et)), Fo(Xihuan’),

go(<↑1>), make(<↓0>), go(<↓0>),

put(?Ty(t))

ELSE Abort

This is why there is what we have called a Double Merge taking place here, there is the subject

of the propositional predicate xihuan and the subject of the 2-place predicate chi that need to be

filled in. Resulting in what we see in step i., where the topic wo functions in both places, which

is why you can say wo xihuan wo chi pingguo in Chinese, however subjects are often left out in

Chinese grammar, they are usually inferred from the context of the given situation.

What we haven’t seen yet is a scenario where Merge needn’t take place because all the tree

nodes are filled in with lexical information. We can see an example of this by analysing the

following 3-place predicate gei which in English means give. Gei has both a subject, object and

secondary object, also known in semantics and syntax as the beneficiary. The other object is

what is given. Take the following sentence in (12) as an example.

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(12) shengdanjie wo gei haizimen liwu le

Christmas I give children presents CRS

(As for/In regards to) Christmas, I gave the children presents.

The sentence in (12) is analysed in the following DS tree structure in (13) below.

(13) Parsing Shengdanjie wo gei haizimen liwu le

Tn(0), ?Ty(t), ASP(CRS)

Fo(Shengdanjie’), Ty(e), Tn(00), ?Ty(e) , Tn(01), ?Ty(et)

<↑*>Tn(0), ?∃x.Tn(x) Fo(Wo’)

Tn(010), Ty(e) Tn(011), Ty(e(et)),

Fo(Haizimen’)

Tn(0110), Ty (e), Tn(0111),

Fo(Liwu’) Ty (e(e(et))),

Fo(Gei’)

What we see here in is that it is not possible for Merge to take place with a 3-place predicate with

all the subject, object and secondary object nodes all filled in. The following lexical input for the

verb gei is given below.

/kei˨˩˦/

IF ?Ty(et)

THEN make(<↓1>), go(<↓1>),

put(Ty(e(et)),

make(<↓1>), go(<↓1>),

put (Ty(e(e(et))), Fo(Gei’)

go(<↑1>), make(<↓0>), go(<↓0>),

put(?Ty(e)), go(<↑0>),

go(<↑1>), make(<↓0>), go(<↓0>),

put(?Ty(e))

ELSE Abort

3.0 Summary

With the tools that DS has given us we are able to parse complex strings of information and

through this step-by-step process we can see where the topic is being added, how this is done and

the nature of topics in general. What Mandarin Chinese is doing is that it is using the steps of

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*Adjunction to add topic information to the sentence. Later, we see through the steps of Merge

where the topic, sometimes but not always, fills in missing information that has to be inferred

from the context under which the sentence is uttered.

In order to understand more about Mandarin Chinese and topics uttered and analysed through the

tools of DS more research is needed. Then we will be able to see more about the nature of how

topics are uttered and parsed in Mandarin Chinese. Hopefully this short study has begun what

other future studies can continue.

4.1 Appendix

*Adjunction (Rule)

{…{{Tn(a), …, ?Ty(t), ◊}}…}

{…{{Tn(a), …, ?Ty(t)},{<↑*>Tn(a), ?∃xTn(x), …, ?Ty(e), ◊}}…}

Introduction – Subject and Predicate

{{…{Tn(n), ?Ty(t), ◊}}

{…{Tn(n), ?Ty(t), ?<↓0>Ty(e), ?<↓1>Ty (et), ◊}}

Prediction – Subject and Predicate

{…{Tn(0), ?<↓0>Ty(e), ?<↓1>Ty(et), ◊}}

{…{Tn(0), ?<↓0>Ty(e), ?<↓1>Ty (et)}, {<↑0>Tn(0), ?Ty(e), ◊},

{<↑1>Tn(0), ?Ty(et)}}

Merge

{…{…DU, DU’…}…}

{…{…DU ⊔ DU’…}…}

◊ ∈ DU’

4.2 Bibliography

Cann, R., Kempson, R. and Marten L. (2005). The Dynamics of Language: An Introduction.

Syntax and Semantics volume 35. San Diego: Elsevier Academic Press.

Kempson, R, Meyer-Viol, W. and Gabbay D. (2001). Dynamic Syntax: The Flow of Language

Understanding. Oxford: Blackwell Publishers, King’s College London.

Li, C. and Thompson S. (1989). Mandarin Chinese: A Functional Reference Grammar. London:

University of California Press.