BUILDING AND EXPLOITING SYNTACTICALLY ANNOTATED CORPORABUILDING AND EXPLOITING SYNTACTICALLY ANNOTATED CORPORA

Edited by
ANNE ABEILLE

University of Paris

Kluwer Academic Publishers
Boston/Dordrecht/London

Contents

List of Figuresvii

List of Tables
1
BUILDING A TURKISH TREEBANK
Kemal Oflazer Bilge Say Dilek Zeynep Hakkani-Tur Gokhan Tur
1. Turkish: Morphology and Syntax
2. What information needs to be represented?
3. The Annotation Tool
4. Some DiÆcult Issues
5. Conclusions and Future Work
Appendix: Turkish Morphological Features

ix

v

1
2
4
11
13
14
16

List of Figures

1.1
1.2
1.3
1.4
1.5
1.6
1.7

Links and In ectional Groups
Example Turkish Sentence
Dependency structure for a sample Turkish Sentence
Sample treebank encoding a Turkish sentence
The user interface of the treebank annotation tool
Selecting the link type
Linking conjoined constituents

vii

8
8
9
10
12
13
14

List of Tables

1.1

Parse and IG Statistics from a Turkish Corpusix

6Chapter 1

BUILDING A TURKISH TREEBANK
Kemal Oflazer
Faculty of Engineering and Natural Sciences
Sabanc University_
Istanbul,
Turkeyo azer@sabanciuniv.edu

Bilge Say
Informatics InstituteMiddle East Technical University
Ankara, Turkey

bsay@ii.metu.edu.tr

Dilek Zeynep Hakkani-TurFaculty of Engineering and Natural Sciences
Sabanc University_
Istanbul,
Turkeyhakkani@sabanciuniv.edu

Gokhan T
ur
Department of Computer Engineering
Bilkent University
Ankara, Turkey

tur@cs.bilkent.edu.tr
Abstract:

We present the issues that we have encountered in designing a treebank architecture for Turkish along with rationale for the
choices we have made for various representation schemes. In the resulting representation, the information encoded in the complex agglutinative
word structures are represented as a sequence of in ectional groups separated by derivational boundaries. The syntactic relations are encoded as
1

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SYNTACTICALLY ANNOTATED CORPORA

labeled dependency relations among segments of lexical items marked byderivation boundaries. Our current work involves re ning a set of treebank annotation guidelines and developing a sophisticated annotation
tool with an extendible plug-in architecture for morphological analysis,morphological disambiguation and syntactic annotation disambiguation.
Keywords:

Treebanks, Dependency Syntax, Turkish, Agglutinative Languages

Introduction

In the last few years, treebank corpora such as the Penn Treebank[12, 13] or the Prague Dependency Treebank [2] have become a crucial
resource for building and evaluating natural language processing tools
and applications. Although the compilation of such structurally annotated corpora is time-consuming and expensive, the eventual bene ts
outweigh this initial cost. With a set of future applications in mind, we
have undertaken the design of a treebank corpus architecture for Turkish,which we believe encodes the lexical and structural information relevant
to Turkish. In this chapter we present the issues that we have encountered in designing a treebank for Turkish along with rationale for the
representation choices we have made. In the resulting representation,
the information encoded in the complex agglutinative word structures isrepresented as a sequence of in ectional groups separated by derivationalboundaries. A tagset reduction is not attempted as any such reduction
leads to removal of potentially useful syntactic markers, especially inthe encoding of derived forms. At the syntactic level, we have opted tojust represent relationships between lexical items (or rather, in ectionalgroups) as dependency relations. The representation is extensible sothat relations between lexical items can be further re ned by augmenting syntactic relations using ner distinctions which are more semantic
in nature.
1.

TURKISH: MORPHOLOGY ANDSYNTAX

Turkish is an Ural-Altaic language, having agglutinative word structures with productive in ectional and derivational processes. Derivational phenomena have rarely been addressed in designing tagsets, andin the context of Turkish, this may pose challenging issues, as the number of forms one can derive from a root form may be in the millions[8].

BUILDING A TURKISH TREEBANK

3

Turkish word forms consist of morphemes concatenated to a root morpheme or to other morphemes, much like beads on a string. Except fora very few exceptional cases, the surface realizations of the morphemes
are conditioned by various morphophonemic processes such as vowel harmony, vowel and consonant elisions. The morphotactics of word forms
can be quite complex when multiple derivations are involved. For instance, the derived modi er saglamlastrdgmzdaki1 would be represented as:2
saglam+Adj^DB+Verb+Become^DB+Verb+Caus+Pos^DB+Adj+PastPart+P1sg^DB+Noun+Zero+A3sg+Pnon+Loc^DB+AdjMarking such a word as an adjective and ignoring anything that comes
before the last part of speech would ignore the fact that the stem is also
an adjective which may have syntactic relations with preceding words
such as an adverbial modi er, or that there is an intermediate causative(hence transitive) verb which may have an object NP or a subject NP
to its left.
A recent experiment that we conducted on about 250,000 Turkishwords in news text revealed that there were over 6,000 distinct morphological feature combinations when root morphemes were ignored.
Although this is less than the much larger numbers quoted by Hankamer who considered the generative capacity of the derivations, it isnevertheless much larger than the distinctions encoded by the tagsets oflanguages like English or French. What is important is not the size ofthe potential tagset, but rather
the fact that there is no a priori limit on it as the next set of million
words that one looks at may contain another 6,000 distinct feature
combinations, andthe nature of the derivational information.
On the syntax side, although Turkish has unmarked SOV constituent
order, it is considered a free-constituent order language as all constituents
including the verb, can move freely as demanded by the discourse context with very few syntactic constraints [4]. Case marking on nominal
constituents usually indicates their syntactic role. Constituent orderin embedded clauses is substantially more constrained but deviations

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SYNTACTICALLY ANNOTATED CORPORA

from the default order, however infrequent, can still be found. Turkish
is also a pro-drop language, as the subject, if necessary, can be elided
and recovered from the agreement markers on the verb. Within nounphrases, there is a loose order with speci ers preceding modi ers, but
within each group, order (e.g., between cardinal and attributive modi ers) is mainly determined by which aspect is to be emphasized. For
instance the Turkish equivalents of two young men and young two men
are both possible: the former being the neutral case or the case where
youth is emphasized, while the latter is the case where the cardinality
is emphasized. A further but relatively minor complication is that various verbal adjuncts may intervene in well-de ned positions within NPs
causing discontinuous constituents.
2.

WHAT INFORMATION NEEDS TO BE
REPRESENTED?

We expect this treebank to be used by a wide variety of \consumers",
ranging from linguists investigating morphological structure and distributions, syntactic structure, constituent order variations, to computational linguists extracting language models or evaluating parsers, etc.
We would therefore employ an extendable multi-tier representation, sothat any future extensions can be easily incorporated if necessary. Similar concerns have also been addressed in the French Treebank [1].
2.1

REPRESENTING MORPHOLOGICALINFORMATIONAt the lowest level we would like to represent three main aspects of alexical item:
The word itself, e.g., evimdekiler, (those in my house).
The lexical structure, as a sequence of free and bound morphemes(including any morphophonological material elided on the surface,and meta symbols for relevant phonological categories), e.g.,
ev+Hm+DA+ki+lAr(where for instance D represents a set of dental consonants, H a setof high-vowels and A represents the set of non-round front vowels,
which are resolved to their surface realizations when the phonological context is taken into account.)
The morphological features encoded by the word as a sequence ofmorphological and POS feature values all of which except the root
are symbolic, e.g.,

BUILDING A TURKISH TREEBANK

5

ev+Noun+A3sg+P1sg+Loc^DB+Adj^DB+Noun+Zero+A3pl+Pnon+Nom

A point to note about this representation is that, information thatis conveyed covertly by zero-morphemes that is not explicit in thelexical representation, is represented here. (e.g., if a plural marker
is not present then the noun is singular hence +A3sg is the feature
supplied even though there is no overt morpheme.) A comprehensive list of morphological feature symbols is given in Appendix
A.
The rst two components of the morphological information do notdeserve any more details for the purposes of this presentation. The
third component with its relation to lexical tag information needs to bedetailed further.
The prevalence of productive derivational word forms brings a challenge to representing such information using a nite (and possibly reduced) tagset. The usual approaches to tagset design, typically assume
that the morphological information associated with a word form can be
encoded using a nite number of cryptically coded symbols from some
set whose size ranges from few tens (e.g., Penn Treebank tag set [12]) to
hundreds or even thousands (e.g., Prague Treebank tagset, [5, 2]). But,such a nite tagset approach for languages like Turkish inevitably leads
to loss of information. The reason for this is that the morphologicalfeatures of intermediate derivations can contain markers for syntactic
relationships. Leaving out this information within a xed-tagset scheme
may prevent crucial syntactic information from being represented.
For these reasons we have decided not to compress in any way themorphological information associated with a Turkish word and represent such words as a sequence of in ectional groups (IGs hereafter),
separated by ^DBs denoting derivation boundaries. Thus a word would
be represented in the following general form:
root+Infl1^DB+Infl2 ^DB+  ^DB+Infln

where Infli denote relevant in ectional features including the part-ofspeech for the root or any of the subsequent derived forms, if any. For instance, the derived modi er saglamlastrdgmzdaki (with the parse
given earlier) would be represented by the 6 IGs:
1. saglam+Adj
2. +Verb+Become
3. +Verb+Caus+Pos
4. +Adj+PastPart+P1sg
5. +Noun+Zero+A3sg+Pnon+Loc 6. +Adj
Note that the set of possible IGs is nite and these can be compactly
coded into (cryptic) symbols, but we feel that apart from saving storage,such an encoding serves no real purpose while the resulting opaquenessprevents facilitated access to component features.

6

SYNTACTICALLY ANNOTATED CORPORA

Although we have presented a novel way of looking at the lexical
structure, the reader may have received the impression that words inTurkish have overly complicated structures with many IGs per word.
The situation as indicated by various statistics actually indicate that
this is really not the case. For instance the statistics presented in Table
1.1, compiled from about 850,000 word corpus of Turkish news text
indicate that on the average the number of IG's per words is less than
2. Thus, for instance modelling each word uniformly with 2 IGs may be
a very good approximation for statistical modeling [6].
Table 1.1

Parse and IG Statistics from a Turkish CorpusAll

All but high frequency

tokensfunction words andand punctuation

Morph. Parses per Token

1.76

1.93

IGs per Parse% Tokens with single parse

1.38
55

1.48
45

% Parses with 1 IG
% Parses with 2 IGs% Parses with 3 IGs% Parses with > 3 IGs

72
18
7
3

65
23
9
3

7
2448

7

Max Number of IGs in a parse
Distinct IGs ignoringroots

Turkish is also very rich in lexicalized and non-lexicalized collocations[16, 17]. The lexicalized collocations are much like what one would nd
in other languages. On the other hand, non-lexicalized collocations canbe divided into two groups:
1. In the rst group, we have compound and support verb formations where there are two or more lexical items the last of which
is a verb. Even though the other components can themselves bein ected, they can be assumed to be xed for the purposes of
the collocation and the collocation assumes its in ectional featuresfrom the in ectional features of the last verb which itself may undergo any morphological derivation or in ection process. For instance, the idiomatic verb kafa cek- (kafa+Noun+A3sg+Pnon+Nom

BUILDING A TURKISH TREEBANK

7

cek+Verb+: : : ) (literally, to pull head) means to get drunk, andthese two tokens essentially behave together as far as syntax goes.3

2. The second group of non-lexicalized collocations involve full or
partial duplication of verb, adjective or noun forms. For instance,
the aorist marked verb sequence
gelir gelmez (gel+Verb+Pos+Aor+A3sg gel+Verb+Neg+Aor+A3sg)
actually functions as a temporal adverbial meaning as soon as: : : comes. Note that these formations (usually involving full or
partial reduplications of strings of the sort ! !, ! x ! or !x !z )are beyond the formal power of nite state mechanisms, hence
are not dealt within the nite state morphological analyzer. (SeeOflazer and Kuruoz [16] or Oflazer and Tur [17], for a list of such
non-lexicalized collocations.)
2.2

REPRESENTING SYNTACTIC
RELATIONS

We would like to represent syntactic relations between lexical items(actually between in ectional groups as we will see in a moment) using
a simple dependency framework. Our arguments for this choice essentially parallels those of recent works on this topic [5, 2, 19, 3, 10]. Free
constituent ordering and discontinuous phrases make use of constituentbased representations rather diÆcult and unnatural to employ. It is
however possible to use constituency where it makes sense and bracket
sequences of tokens to mark segments in the texts whose internal dependency structure would be of little interest. For instance, collocations,time{date expressions or multiword proper names (which incidentally
do not follow Turkish noun phrase rules so have to be treated speciallyanyway) are examples whose internal structure is of little syntactic concern, and can be bracketed a priori as chunks and then related to otherconstituents. Such features have also been proposed for the French Treebank [1]. If necessary, any further constituent-based representation canbe extracted from the dependency representation [11].
An interesting observation that we can make about Turkish is that,when a word is considered as a sequence of IGs, syntactic relation links
only emanate from the last IG of a (dependent) word, and land on oneof the IGs of the (head) word on the right (with minor exceptions), as
exempli ed in Figure 1.1. A second observation is that, (again withminor exceptions), the dependency links between the IGs, when drawn
above the IG sequence, do not cross (although this is not a concernhere).4 Figure 1.3 shows a dependency tree for the following sentence

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SYNTACTICALLY ANNOTATED CORPORA

Figure 1.1

Links and In ectional Groups

Links from Dependents

IG1

IG2

+

Link to Head

+

IG3

+

IG4

Word

in Figure 1.2, laid on top of the words segmented along IG boundaries.
Note for instance that, for the word buyumesi the previous two words
link to its rst (verbal) IG, while its 2nd IG (in nitive nominal) links to
the nal verb as subject.
Figure 1.2

(1)

Bu

Example Turkish Sentenceeskibahce-de+kibu(this)+Det eski(old)+Adj bahce(garden)+A3sg+Pnon+Loc^DB+Adj

The growth of the rosegul-unboylegul(rose)+Noun+A3sg+Pnon+Gen boyle(like-this)+Adv

like this in this old garden impressed everybody.

buyu+me-sibuyu(grow)+Verb+Pos^DB+Noun+Inf+A3sg+P3sg+Nom

herkes-i

cokherkes(everybody)+Pron+A3sg+Pnon+Acc cok(very)+Adv

etkile-di.

etkile(impress)+Verb+Pos+Past+A3sg

The syntactic relations that we have currently opted to encode in our
syntactic representation are the following:BUILDING A TURKISH TREEBANK

9

1. Subject
2. Object
3. Modi er (adv./adj.) 4. Possessor
5. Classi er
6. Determiner
7. Dative Adjunct
8. Locative Adjunct9. Ablative Adjunct10. Instrumental AdjunctSome of the relations above perhaps require some more clari cation.
Object is used to mark objects of verbs and the nominal complements ofpostpositions. A classi er is a nominal modi er in nominative case (as inbook cover ) while a possessor is a genitive case-marked nominal modi er.
For verbal adjuncts, we indicate the syntactic relation with a marker
paralleling the case marking though the semantic relation they encode
is not only determined by the case marking but also the lexical semanticsof the head noun and the verb they are attached to. For instance a dative
adjunct can be a goal, a destination, a bene ciary or a value carrier ina transaction, or a theme, while an ablative adjunct may be reason, asource or a theme. Although we do not envision the use of such detailed
relation labels at the outset, such distinctions can certainly be usefulin training case-frame based transfer modules in machine translation
systems to select the appropriate prepositions in English for instance.
Figure 1.3

Dependency structure for a sample Turkish Sentence

Det

Subj
Mod

Mod

Subj

Obj
Mod

Mod

Bu eski bahçe-de+ki gül-ün böyle büyü +me-si herkes-i çok etkile-di
D

ADJ

N

ADJ

N

ADV

V

N

PN

ADV

Last line shows the nal POS for each word.
2.3

EXAMPLE OF A TREEBANKSENTENCE

In this section we present the detailed representation of a Turkish
sentence in the treebank. Each sentence is represented by a sequenceof attribute lists of the words involved, bracketed with tags <S> and</S>.5 Figure 1.4 shows the treebank encoding for the sentence given
earlier. Each word is bracketed by <W> and </W> tags. The IX denotes
the number or index of the word. LEM denotes the lemma of the word,
as one would nd in a dictionary. For verbs, this would typically be an
in nitive form, while for other word classes it would usually be the rootV10

SYNTACTICALLY ANNOTATED CORPORA

Figure 1.4

Sample treebank encoding a Turkish sentence<S><W IX=1 LEM="bu" MORPH="bu" IG=[(1, "bu+Det")] REL=[(3,1,(DETERMINER)]>Bu </W><W IX=2 LEM="eski"' MORPH="eski" IG=[(1, "eski+Adj")]
REL=[3,1,(MODIFIER)]> eski> </W>
<W IX=3 LEM="bah
ce" MORPH="bahce+DA+ki" IG=[(1, "bah
ce+A3sg+Pnon+Loc")(2, "+Det")] REL=[4,1,(MODIFIER)]> bah
cedeki </W>
<W IX=4 LEM="g
ul" MORPH="g
ul+nHn" IG=[(1,"g
ul+Noun+A3sg+Pnon+Gen")]
REL=[6,1,(SUBJECT)]> gul
un </W><W IX=5 LEM="b
oyle" MORPH="boyle" IG=[(1,"b
oyle+Adv")]
REL=[6,1,(MODIFIER)]> boyle </W><W IX=6 LEM="buyumek" MORPH="buy
u+mA+sH" IG=[(1,"b
uyu+Verb+Pos") (2,"+Noun+Inf+A3sg+P3sg+Nom")] REL=[9,1,(SUBJECT)]> buy
umesi </W>
<W IX=7 LEM="herkes" MORPH="herkes+yH" IG=[(1,"herkes+Pron+A3sg+Pnon+Acc")]
REL=[9,1,(OBJECT)]> herkesi </W>
<W IX=8 LEM="cok" MORPH="cok" IG=[(1,"
cok+Adv'')] REL=[9,1,(MODIFIER)]>cok </W><W IX=9 LEM="etkilemek" MORPH="etkile+DH" IG=[(1,"etkile+Verb+Pos+Past+A3sg")] REL=[]> etkiledi </W></S>

word itself. MORPH indicates the morphological structure of the word
as a sequence of morphemes, essentially corresponding to the lexicalform. The morphemes may involve meta-symbols (mentioned earlier)for indicating any phonological classes of symbols. IG is a list of pairsof an integer and an in ectional group. REL encodes the relationshipof this word, as indicated by its last in ection group, to an in ectionalgroup of some other word. The rst component of REL is the index ofa word, the second component is the number of the in ection group inthat word that this word's last IG is linked to, and the third component
is a list of relation labels for any possible syntactic (e.g., dative adjunct)and semantic (e.g., destination),relationships between the IGs involved.
For example, the 4th and 5th words in the sentence are subject andand adverbial modi er, respectively, of the verb in the rst IG of the
6th word, while the 2nd IG of the same word (6) is the subject of themain verb of the word 9. We have only used simple syntactic relation

BUILDING A TURKISH TREEBANK

11

names in the example but more certainly can be added. For instanceadjective modi ers can be further classi ed into attributive, cardinal,etc., while an object may further be marked as theme or patient, as
discussed earlier.
A collocation would be represented by coalescing the information of individual components. For instance, the non-lexicalized collocation gelir
gelmez and its adjunct
(2)

ev+e

gel+irev+Noun+A3sg+Pnon+Dat gel+Verb+Pos+Aor+A3sg
gel+Verb+Neg+Aor+A3sg

gel+me+z: : : as soon as : : : comes to the house : : :would be represented as...
<W IX=5 LEM="ev" MORPH="ev+yA" IG=[(1,"ev+A3sg+Pnon+Dat")]],
REL=[6,1,(DATIVE-ADJ,DEST)]> eve </W>
<W IX=6 LEM="gelmek" MORPH="gel+Hr gel+mA+z"
IG=[(1, "gel+Verb+Pos")(2, "+Adv+AsSoonAs")],
REL=[...]> gelir gelmez </W>...

where it should be noted that the non-lexicalized collocation has beentreated as derivational process and an adverbial IG +Adv+AsSoonAs has
been created.
3.

THE ANNOTATION TOOL

We have implemented a rst version of treebank annotation tool thatlets an annotator semi-automatically annotate a Turkish text. A snapshot of the user interface of this tool is given in Figure 1.5.
On the top, the annotator sees the sentence as text along with theprevious and the next sentences, if any. The main window below contains
the morphological analyses of the tokens with ambiguous analyses being
listed vertically below the token. The annotator then performs a manualmorphological disambiguation by selecting the appropriate analysis witha tick box.6 The IGs of the selected analysis are then listed side by side,on the middle of the lower window, with the morphological features in
an IG being listed vertically (see the entries above the rightmost word
bracketed with ==). The annotator then proceeds with a drag and dropinteraction, clicking on a source IG, starting a link and then drops theend of the link on the target IG. At this point a pop-up menu forces the12

SYNTACTICALLY ANNOTATED CORPORA

Figure 1.5

The user interface of the treebank annotation tool

BUILDING A TURKISH TREEBANK

Figure 1.6

13

Selecting the link type

annotator to select a link type as shown in Figure 1.6. In a future version,
this linking will be done in a more intelligent fashion with the destination
IG and the contents of the label popup menu being determined by thesource IG.
4.

SOME DIFFICULT ISSUES

Turkish is a pro-drop language, and subject (and usually various otherconstituents) may be elided on the surface. In the case of subjects,
the information is recoverable from the agreement marker on the verbs.
Since we aim to capture just the surface relations, such covert cases are
not marked. The cases for verb ellipsis is a bit more tricky. In thesecases we have constituents which do not have a surface governor. We
have for the time being opted to capture these cases by explicitly enteringa dummy constituent (with a null surface form but nevertheless beinga token) linked with a special link to the parallel verb, indicating its
ellipsis status. Then the contituents of the elided verb can be attachedto this dummy constituent.

14

SYNTACTICALLY ANNOTATED CORPORA

Linking conjoined constituents

Figure 1.7

Subj
Mod Mod

Mod

Subj Subj

Yorgun ve zgn adam ve
Tired

and

sad

man

Dest

ocuk eve

gittiler.

and child home went.

The tired and sad man and the child went home.
Mod
Mod Mod

Subj

Subj Subj

Yorgun ve zgn adam veDestocuk eve

gittiler.

The tired and sad man and child went home.

Headless constructions such as coordinating conjunctions have been
one of the weaker points of dependency grammar approaches. Our solution for describing coordinate conjunction constructs essentially follows
Jarvinen and Tapanainen [9]. For a sequence of IGs likeD1 : : : C : : : D2 : : : C : : : : : : Dk : : : H
where Di are the dependent IGs that are coordinated and C s are theconjunction IGs (for , (comma), and and or ), and H is the head IG, we
e ectively thread a \long link" from D1 to H . If the link between Dk
and H is labeled with l, then dependent Di links to the following C withlink l, and this C links to Di+1 with l. One feature of Turkish simpli es
this threading a bit: the left conjunct IG has to immediately precede theconjunction IG (except for the very unlikely cases of verbal coordination
in inverted constituent orders). Figure 1.7 shows the links for encoding
two possible interpretations of conjunction scopes for a simple Turkish
sentence.
5.

CONCLUSIONS AND FUTURE WORKAs we mentioned at the outset, our current work has concentrated on
resolving the issues in encoding Turkish treebanks. There are certainly
other theoretical issues especially in the dependency representations ofvarious problematic constructs. We have also completed the implementation a rst version of an annotation tool for compiling Turkish treebanks.
Our current work involves developing and re ning a set of guidelines
for annotation Turkish text using this framework and developing a theBUILDING A TURKISH TREEBANK

15

nal speci cations of the second version of the annotation tool basedon the experience gained from building and experimenting with the current tool. The new tool will integrate tools that we have already developed for tokenization, morphological analysis, collocation processing
and morphological disambiguation [14, 18] as plug-ins in an extensible
way. We also expect to utilize our statistical disambiguator module [6]and integrate a dependency parser (e.g., [15]) to generate full or partial
dependency parses and have a human operator disambiguate and correct
the parses if necessary.
Acknowledgments

 ITAK,_
This work is supported by a grant from TUB
the Turkish Scienti c andTechnical Research Council by Grant No: EEEAG-199E026.

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SYNTACTICALLY ANNOTATED CORPORA

Appendix: Turkish Morphological Features

In this section we provide a list of morphological features used in
the encoding of about 9,000 possible IGs that can be produced by ourmorphological analysis. Although not all of these have been used inexamples used in this chapter, we feel it is useful for conveying to thereader the wealth of the information Turkish lexical forms encode.
Major Parts of Speech:+Noun, +Adj, +Adv, +Conj, +Det,+Dup, +Interj, +Ques, +Verb, +Postp, +Num, +Pron, +Punc.
(+Dup category contains onomatopoeia words which only appear as

duplications in a sentence.)

Minor Parts of Speech: These typically follow a major POS tofurther subdivide that class, or to indicate the kind of derivation
involved.
{ After +Num: +Card, +Ord, +Percent, +Range, +Real, +Ratio,+Distrib, +Time.
{ After +Noun: +Inf, +PastPart, +FutPart, +Prop, +Zero.
{ After +Adj: +PastPart, +FutPart, +PresPart.
{ After +Pron: +DemonsP, +QuesP, +ReflexP, +PersP, +QuantP.

The following (mostly semantic) markers are used after derivations
to indicate the kind of derivation involved:{ After +Adv derived from verbs: +AfterDoingSo, +SinceDoingSo,+As (he does it), +When, +ByDoingSo, +While, +AsIf,+WithoutHavingDoneSo.
{ After +Adv derived from Adjectives: +Ly (equivalent to theEnglish +ly derivation.){ After +Adv derived from temporal nouns: +Since{ After +Adj derived from nouns: +With, +Without +SuitableFor,+InBetween, +Rel.
{ After +Noun derived from adjectives: +Ness (as in red vs.
redness){ After +Noun derived from nouns: +Agt (someone involved insome way with the stem noun), +Dim (Diminutive),{ After +Verb derived from nouns or adjectives: +Become (tobecome like the noun or adjective in the stem) +Acquire (toacquire the noun in the stem){ A +Zero appears after a zero morpheme derivation.

Appendix

A

17

Nominal forms (Nouns, Derived Nouns, Pronouns, Participles andIn nitives) get the following additional in ectional markers:
1. Number/Person Agreement: +A1sg, +A2sg, +A3sg, +A1pl,+A2pl, +A3pl.
2. Possessive Agreement: +P1sg, +P2sg, +P3sg, +P1pl,+P2pl, +P3pl, +Pnon (no overt agreement).
3. Case:+Nom, +Acc, +Dat, +Abl, +Loc, +Gen, +Ins.
Adjectives (lexical or derived) do not take any in ection, except+Adj+PastPart and +Adj+FutPart will have a +Pxxx (possessive
agreement as above) to mark verbal agreement. Any other in ection to adjectives implies type-raising to nouns and the in ection
goes onto the noun after a 0-morpheme derivation.
Verbs have two sets of markers which are treated as derivations:
1. Voice: +Pass, +Caus, +Reflex +Recip, (A verb form may
have multiple causative markers).
2. Compounding/Modality: +Able (able to verb), +Repeat(verb repeatedly), +Hastily (verb hastily), +EverSince (havebeen verb-ing ever since), +Almost (almost verb-ed but didnot), +Stay (stayed frozen while verb-ing), +Start (startverb-ing immediately)
Verbs also get the following in ectional markers:
1. Polarity: +Pos, +Neg
2. Tense-Aspect-Mood: A nite verb may have 1 or 2 of
+Past (past tense), +Narr (narrative past tense), +Fut (future tense), +Aor (Aorist, may indicate habitual, present, future, you name it), +Pres (present tense, for predicative nominals or adjectives), +Desr (desire/wish), +Cond (conditional),+Neces (Necessitative, must), +Opt (optative, let me/him/herverb), +Imp (imperative), +Prog1 (Present continuous, process), +Prog2 (Present continuous, state).
3. Verbs also have number person agreement markers (see nominal forms earlier) and an optional copula marker.
Notes

1. Literally, \(the thing existing) at the time we caused (something) to become strong".
Obviously this is not a word that one would use everyday. Turkish words (excluding noninecting frequent words such as conjunctions, clitics etc) found in typical text average about
10 letters in length.

18

SYNTACTICALLY ANNOTATED CORPORA

2. Please refer to the comprehensive list of morphological features given in Appendix A
for the semantics of some of the non-obvious symbols used here.
3. Though they may be separated by various clitics, in which case the collocation can not
be recognized by simple local means.
4. This however does not mean that there no non-projective constructs in Turkish. There
are a number of constructs, such as an adverbial modifying a verb, cutting in between amodi er and the head noun making up the subject NP. These, however, are very rare. Ourrepresentation does not have any restriction regarding projectivity and lets us represent thecrossing links in such case.
5. Words in this context may also be a lexicalized or non-lexicalized collocations.
6. The input to the annotator is actually morphologically preprocessed with each token
already having been analyzed in all its ambiguities. This same le could also be run througha morphological disambiguator module [7]. If this disambiguator makes any mistakes (andthey do), our current tool does not let us correct an incorrectly disambiguated morphologicalanalyses yet, so we have opted not to disambiguated for the time being.
References[1] A. Abeille, L. Clement, and A. Kinyon. Building a treebank for
french. In A. Abeille, editor, Building and Exploiting Syntatically Annotated Corpora, Text, Speech and Language Technology.
Kluwer, 2001.
[2] A. Bemova, J. Hajic, B. H. J. Panenova, A. Bohmova, and E. Hajicova. The Prague Dependency Treebank. In A. Abeille, editor, Building and Exploiting Syntatically Annotated Corpora, Text,
Speech and Language Technology. Kluwer, 2001.
[3] T. Brants, W. Skut, and H. Uszkoreit. Syntactic annotation ofa german newspaper corpus. In A. Abeille, editor, Building andExploiting Syntatically Annotated Corpora, Text, Speech and Language Technology. Kluwer, 2001.
[4] E. Erguvanl. The Function of Word order in Turkish. PhD thesis,
University of California, Los Angeles, 1979.
[5] J. Hajic. Building a syntactically annotated corpus: The Prague
Dependency Treebank. In E. Hajicova, editor, Issues in Valency
and Meaning: Studies in Honour of Jarmila Panenova. Karolinum{ Charles University Press, Prague, April 1998.
[6] D. Z. Hakkani-Tur. Statistical Language Modeling for Turkish.
PhD thesis, Bilkent University, Department of Computer Science,
Ankara, Turkey, 2000.
[7] D. Z. Hakkani-Tur, K. O azer, and G. Tur. Statistical morphological disambiguation for agglutinative languages. In Proceedings of
COLING 2000. ICCL, August 2000.
[8] J. Hankamer. Morphological parsing and the lexicon. In
W. Marslen-Wilson, editor, Lexical Representation and Process.
MIT Press, 1989.

Appendix

A

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