Consistent Grammatical Annotation of Turkic Languages for more
universal Universal Dependencies
Chihiro Taguchi
University of Edinburgh / Nara Institute of Science and Technology
Edinburgh, United Kingdom / Nara, Japan
1

Introduction

Recent advance in natural language processing (NLP)
has shed light on its universal application to any languages, in particular languages with less resources.
Universal Dependencies (UD) (Nivre et al., 2020) is oneof such cross-linguistic projects that aims to accumulate
annotated linguistic data with the rules that can ideallybe universally applied to and are consistent in any languages. For cross-linguistic applications and research
in NLP and linguistics, it is of paramount importanceto use annotated linguistic data with unified annotation
rules. However, in practice, current UD treebanks contain a number of discords in terms of annotation rules,
even in the same language. This disagreement in the
annotation style hinders simultaneous use of multiple
treebanks because it is likely to cause confusion in, forinstance, supervised tasks and transfer learning.
Given this situation, this study reports current annotation conventions of tokenization, part-of-speech (POS),
and morphological tagging and their conflicts in the UD
treebanks of Turkic languages as a case study. For eachissue, this study provides possible solutions that are linguistically justifiable for unified annotation rules that
commonly apply to Turkic languages.
Annotation consistency assessment in UD has been
quantitatively investigated in de Marneffe et al. (2017)
for UD English, French, and Finnish; however, it solely
evaluates dependency head and relation, and other aspects of UD annotation are trimmed away. Although
Tyers et al. (2017) assesses the guidelines for five treebanks of three Turkic languages in details, the numberof treebanks and Turkic languages in UD has doubled
since its publication. Therefore, it is worthwhile to investigate the (dis)agreements in the annotation across
Turkic UD once again.
1.1

Turkic Languages

The Turkic language family is a genetically relatedgroup of languages distributed across Eurasia, from
Turkish in the west to Sakha (Yakut) and Uyghur inEast Asia, as illustrated in Figure 1. The hypothetical Proto-Turkic, which modern Turkic languages commonly derived from, is estimated to have emerged
around 4500–4000 BCE (Johanson, 2021). Modern
Turkic languages are roughly divided into four groups
and two isolated languages: Southwestern (Turkish, Az-eri, Turkmen, etc.), Northwestern (Tatar, Kazakh, Kyrgyz, etc.), Southeastern (Uyghur, Uzbek, etc.), Northeastern (Sakha, Tuvan, etc.), Chuvash and Khalaj. Despite its broad geographical distribution and long history, Turkic languages are known to share typological, phonological, morphological, syntactic, and lexical characteristics. Most of them commonly have SOV
word order, vowel harmony, inflection and derivation
by agglutinative suffixation, and shared lexicon. For
this reason, building multilingual treebanks for Turkic
languages may enable not only applications in NLP but
also quantitative studies in linguistics. However, as thefollowing sections show, there is discrepancies in annotation among the Turkic treebanks.
1.2

Turkic UD Treebanks Discussed

In this paper, we will focus on the UD treebanks of Turkic languages published as of version 2.10 (updated in
May 2022). The list of treebanks are shown in Table
1. There are only seven Turkic languages covered incurrent UD. Turkish is by far the most high-resourced
language among the Turkic UD treebanks with morethan 600K tokens in total. Turkish–German SAGT
is a spoken code-switching treebank of Turkish mixed
with German used by the Turkish community in Germany. Old Turkic1 , categorized in the Northeastern Turkic group, is an extinct language used around the 10th
centuries around current Mongolia and Xinjiang, and is
the earliest Turkic language attested in history.

2

IssuesEach UD treebank is supposed to contain at least the following grammatical information: token (WORD), lemma(LEMMA), part-of-speech (UPOS), morphological features(FEATS), head of the word (HEAD), and dependency relation (DEPREL). In this study, we will particularly delve
into differences in tokenization, UPOS, and FEATS, because their annotation tend to vary among the treebanks
and also they play significant roles in training for tagging tasks.
1
Though the Tonqq treebank uses Old Turkish as the language name, this study refers to it as Old Turkic because
the latter is a more common convention in Turkic linguistics;
also, the former can falsely remind us of a direct ancestor of
the modern Turkish language, which is comparatively distantfrom Old Turkic.

Figure 1: Distribution of Turkic languages.
2.1

Tokenization

Among Turkic UD treebanks, there are two issues intokenization that has yet to be agreed upon: whether
to tokenize a suffix -ki (e.g., Turkish GB) as a separate
token and whether to tokenize all suffixes (morphemes)
as different tokens (e.g., Old Turkic Tonqq).
Adjectivizer -ki.
Many Turkic languages have an adjectivizer suffix -ki(or phonologically corresponding forms). It is used fora locative noun to modify a following noun, as exemplified in an example in Turkish (1). However, because
it is possible to treat the adjectivized form as a noun, it
theoretically allows a recursive use of the suffix (2).
(1) Berlin-de-ki (kişi)
Berlin-LOC - KI person‘(The person) in Berlin’(2) [[Berlin-de-ki]-ler-de-ki]-ler-...
Berlin-LOC - KI - PL - LOC - KI - PL -...
‘... those that are at those in Berlin’s’Considering that UD’s morphological features (FEAT)
do not allow for hierarchical annotation, Turkic treebanks such as GB and SAGT use a trick to handle thisrecursion. These treebanks retokenize -ki as a separate
token and gives it an ADP (adposition) tag, as shownin Table 2. While this policy can avoid the recursion
problem, there are still remaining issues. First, it does
not seem to be possible to justify treating this particularsuffix -ki as a separate token but not others. Second, it
is questionable to tag ki with ADP even though its function is an adjectivalizer. This function seems rather suitable to be annotated as amod (adjectival modification) in

DEPREL as in Table 3. The limitation in the expressiveness of morphology is caused by UD’s architecture thatannotates FEAT only linearly. It can be fixed by allowing
for hierarchical morphological annotation that has been
attempted in Unimorph 4.0 (Batsuren et al., 2022).
Old Turkic.
The tokenization policy in the Old Turkic Tonqq treebank is fundamentally different from other Turkic UD
treebanks. Because Old Turkic texts written in the Old
Turkic script do not explicitly show word segmentationby spacing unlike other modern Turkic orthographies,
conventional approaches of tokenization do not apply
to Old Turkic. Instead, the segmentation policy takenin the Tonqq treebank is to segment every morpheme(Derin and Harada, 2021). This is the same tactic usedin UD treebanks of Japanese, which also does not haveorthographical system for delimiting words. For example, a cognate inflected finite verb сагындым ‘I had
a longing (for)’ in Tatar or saqntm ‘I thought over’ in
Old Turkic are annotated as (4) and (5), respectively.
It is controversial as to which tokenization is linguistically more suitable; however, in practice, there areseveral weaknesses in the morpheme-splitting tokenization. First, other Turkic languages in UD do not split
by morpheme boundaries. Therefore, splitting by morphemes can cause inconsistency across the Turkic UD
treebanks. Second, this policy makes the difference between an independent word and a bound morpheme ambiguous. Third, as one can see by comparing Tables 4
and 5, the latter virtually tells us nothing about the morphological features added by the suffixes (tagged as AUX
in the Table).

Group

SW

SE
NW
NE

Language

TurkishTurkishGerman
UyghurKazakhTatar
Sakha(Yakut)Old Turkic

Treebank
Kenet (Kuzgun et al., 2022b)
Penn (Cesur et al., 2022)
Tourism (Kuzgun et al., 2022a)
Atis (Köse and Yıldız, 2022)
FrameNet (Marşan et al., 2021)
GB (Çöltekin, 2020)
IMST (Çöltekin et al., 2021)
BOUN (Türk et al., 2020)
PUD (Uszkoreit et al., 2021)
SAGT (Çetinoğlu, 2016)

Latest
v2.10
v2.10
v2.10
v2.10
v2.9
v2.7
v2.8
v2.8
v2.8
v2.10

Tokens
178K
87K
92K
45K
19K
17K
57K
122K
16K
37K

Annotation
ManualManualManualSemi-auto
ManualManualSemi-auto
ManualSemi-auto
ManualSourceDictionary
Penn Treebank
Reviews
ATIS
FrameNet
Grammar book
IMST Treebank
Miscellaneous
PUD
SpokenUDT (Eli et al., 2016)
KTB (Makazhanov et al., 2015)
NMCTT (Taguchi et al., 2022)
YKTDT (Merzhevich and Gerardi, 2021)

v2.8
v2.10
v2.10
v2.10

40K
10K
1K
495

ManualManualManualManualBooks
MiscellaneousNews
Miscellaneous

Tonqq (Derin and Harada, 2021)

v2.10

158

ManualInscriptions

Table 1: Details of the Turkic treebanks available as of UD v2.10. Semi-auto in the Annotation column means thatthe annotation was done by combining an automatic tagging process with manual annotation.
ID
1-2
1
2

FORM
Berlin’deki
Berlin’de
ki

LEMMA

UPOS

DEPREL

Berlin
ki

PROPN
ADP

nmod
case

Table 2: Tokenization and tags of ki as a separate token.
ID
1

FORM
Berlin’deki

LEMMA
BerlinUPOS
PROPN

DEPREL
amod

Table 3: Tokenization and tags of ki as morphologicallysuffixed to the stem.

2.2

Part-of-Speech Tagging

Particle.
In UD, the particle tag PART is rather not recommended
to use; the general guideline notes that it should be
used when no other tags are suitable2 . Turkic treebanks
that use PART are BOUN (Turkish), SAGT (Turkish–
German), UDT (Uyghur), and KTB (Kazakh) as listed
in Table 6.
The general guideline of Turkish UD treebanks defines that they should “use the POS tag PART for theword değil ‘not’ when used to negate a non-predicateword”, and “[i]f değil modifies a predicate, it is marked
as VERB since it functions as a copula and carries otherverbal inflections as well”.3 d However, BOUN, as wellas the treebanks not listed in Table 6, indicate that thisguideline is not strictly followed by them. As for SAGT,
it consistently marks değil with PART regardless of the23

https://universaldependencies.org/u/pos/PART.html
https://universaldependencies.org/tr/pos/PART.html

usage specified above.
What falls into PART in Turkic languages’ treebanks
seems inconsistent and incompatible with other Turkic
treebanks. For example, KTB marks ma (yes-noquestion word) and its harmonized counterparts (ba,me, be) with PART; however, their counterparts in other
Turkic languages, for instance Turkish, are generally
annotated as AUX. Following UD’s general policy thattries to avoid the use of PART, it is favorable to use otheralternatives such as AUX and ADV where applicable.
In fact, by comparing with other Turkic treebanks
that avoid PART, many PART words in Uyghur-UDT
and Kazakh-KTB can be substituted with AUX or ADV.
Since the status of these closed POS categories iscontroversial even in linguistics, what we need for UD
is a unified policy for tagging them. This unification
process is possible through cross-treebank and crosslinguistic comparison and consultation with linguists.
Converb: VERB or ADV?
Converb, also called adverbial participle, is a non-finite
form of a verb and it adverbially modifies other (typically main) predicates (Haspelmath, 1995). In Turkic
languages, it is common to use this form to express
consecutive or simultaneous processes. In Table 7,
we can see that the same converb form is annotatedeither as VERB or ADV. However, since converb is a
productively inflected form of a verb, it is plausible totag it with VERB rather than ADV.
Locative adjectivizer: NOUN/PROPN or ADJ?
A similar problem is also found in the treatment of -ki
following a locative suffix, as we have seen in Section

ID
1

FORM
сагындым

LEMMA
сагын

UPOS
VERB

FORM
Number=Sing|Person=1|Tense=Past|VerbForm=Fin

Table 4: Tokenization of сагындым (Tatar; transliterated as saGı̈ndı̈m) split by space.
ID
1
2
3

FORM
saqn
t
m

LEMMA

UPOS
VERB
AUX
AUX

FORM

Table 5: Tokenization of saqntm (Old Turkic; reconstructed as saqı̈ntı̈m) in Tonqq’s policy. The tokens aretransliterated.

UPOS

Kenet

ADV

Penn
Tourism

ADV
ADV

Atis

ADV

GB

VERB

FrameNet

ADV

FEAT

VerbForm=Conv

Treebank

PART words

BOUN

VERB

BOUN

ki (that)

PUD

ADV

SAGT

değil (not), nicht (not)

IMST

VERB

VerbForm=Conv

UDTde (also), qëni (well), belkim(maybe), epsus (pity), mana (here),
emesmu (not), goya (as if), ëhtimal(probably), etc.

SAGT

VERB

VerbForm=Conv

KTB

VERB

VerbForm=Conv

NMCTT

VERB

VerbForm=Conv

UDT

VERB

VerbForm=Conv

YKTDT

NA

NA

KTB

ma (yes-no question), aw (emphatic), šı̈Gar (probably), qoy (emphatic), etc.

Table 6: List of treebanks that use PART and their PARTwords. Uyghur and Kazakh words are transliterated into
Latin for convenience.
2.1. Though Berlin-de-ki adjectivally modifies a nounat the level of dependency relation, it should be tagged
as PROPN (i.e., the lemma’s POS), because it is a productively inflected from a nominal. However, there areseveral Turkish UD treebanks that tag these as ADJ, suchas Kenet and Penn.
2.3

TreebankMorphological Tagging

Bare noun.
In Turkic languages, the default unmarked noun form isnominative singular (Case=Nom|Number=Sing), and isagreed by 3rd person (Person=3) in verbal morphology.
How each treebank annotates the bare form of a noun
greatly differs among each other as demonstrated inTable 8. Given the fact that the bare form is morphologically unmarked, we may well omit any morphologicalannotation on it. Nevertheless, with the interpretationthat the nominative and singular forms are a part of
the nominal paradigm, we can also exhaustively write
them out, thereby improving the consistency withother declined noun forms. Person=3, on the other
hand, is not a suitable morphological feature for a bare

Table 7: UPOS and FEAT annotation for converbs. An
underscore means no annotation given to the form, andNA means converb is unattested in the corpus.

noun, because the noun in Turkic languages does notmorphologically change with respect to person4 unlike
verbs and personal pronouns. In sum, the annotation ofa bare noun can be minimally blank, but it seems betterto annotate it as Case=Nom|Number=Sing.
Converb.
Returning to the distribution of the annotation for converbs in Table 7, we can find another problem in the
FEAT column. That is, many treebanks of Turkish do
not annotate any morphological information in the FEAT
column. Since UD readily prepares VerbForm=Conv tag
for converbs5 , it is favorable to follow the general guideline.
Furthermore, among the treebanks that mark
VerbForm=Conv, some attempt to distinguishconverb forms by further specifying the morphological feature.
For example, in Kazakh-KTB,
itprepares
Aspect=Imp|VerbForm=Conv
andAspect=Perf|VerbForm=Conv to distinguish two
4

Except for possessor, which is marked by another feature
Person[psor]=.
5
https://universaldependencies.org/tr/feat/VerbForm.html

Treebank
Kenet
Penn
Tourism
Atis
GB
FrameNet
BOUN
PUD
IMST
SAGT
UDT
KTB
NMCTT
YKTDT
tonqq

Case=Nom
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N

Number=Sing
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
N
N
Y
N
N

Person=3
Y
Y
Y
Y
N
Y
Y
Y
Y
N
N
N
N
N
N

Table 8: Comparison of annotation for a bare noun.
forms of converb that function differently in terms ofaspect. This is another issue to be discussed in more
depth in treebanks of languages that have a converb.

3

Concluding Remarks

This study showed that the current Turkic treebanks
contain disagreements and inconsistencies in the rulesof tokenization, UPOS, and FEATS. To these issues, we
also showed favorable solutions based on linguistic evidence and comparisons with other treebanks. The conflicts of annotation in words that are cognate or functionin the same manner should be avoided as much as possible. Therefore, we call on UD contributors for crosslingual and cross-treebank discussions. Also, consultation with linguists specialized in the language or related
languages is indispensable for defining justifiable andplausible rules for consistent annotation. We hope that
coherent tagging across languages and treebanks in UD
will further enhance the universality of UD, thereby enabling UD-based typological studies in linguistics andencouraging cross-treebank and cross-lingual applications in NLP.

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