Statistical Dependency Parsing of Turkish
G¨uls¸en Eryiˇgit
Department of Computer Engineering
Istanbul Technical University
Istanbul, 34469, Turkey
gulsen@cs.itu.edu.tr
Kemal Oflazer
Faculty of Engineering and Natural Sciences
Sabanci University
Istanbul, 34956, Turkey
oflazer@sabanciuniv.edu
Abstract
This paper presents results from the first
statistical dependency parser for Turkish.
Turkish is a free-constituent order lan-
guage with complex agglutinative inflec-
tional and derivational morphology and
presents interesting challenges for statisti-
cal parsing, as in general, dependency re-
lations are between “portions” of words
– called inflectional groups. We have
explored statistical models that use dif-
ferent representational units for parsing.
We have used the Turkish Dependency
Treebank to train and test our parser
but have limited this initial exploration
to that subset of the treebank sentences
withonlyleft-to-right non-crossing depen-
dency links. Our results indicate that the
best accuracy in terms of the dependency
relations between inflectional groups is
obtained when we use inflectional groups
as units in parsing, and when contexts
around the dependent are employed.
1 Introduction
The availability of treebanks of various sorts have
fostered the development of statistical parsers
trained with the structural data in these tree-
banks. With the emergence of the important role
of word-to-word relations in parsing (Charniak,
2000; Collins, 1996), dependency grammars have
gained acertain popularity; e.g., Yamada and Mat-
sumoto (2003) for English, Kudo and Matsumoto
(2000; 2002), Sekine et al. (2000) for Japanese,
Chung and Rim (2004) for Korean, Nivre et al.
(2004) for Swedish, Nivre and Nilsson (2005) for
Czech, among others.
Dependency grammars represent the structure
of the sentences by positing binary dependency
relations between words. For instance, Figure 1
Figure 1: Dependency Relations for a Turkish and
an English sentence
shows the dependency graph of a Turkish and
an English sentence where dependency labels are
shown annotating the arcs which extend from de-
pendents to heads.
Parsers employing CFG-backbones have been
found to be less effective for free-constituent-
order languages where constituents can easily
change their position in the sentence without
modifying the general meaning of the sentence.
Collins et al. (1999) applied the parser of Collins
(1997) developed for English, to Czech, and found
thatthe performance wassubstantially lower when
compared to the results for English.
2 Turkish
Turkish is an agglutinative language where a se-
quence ofinflectional andderivational morphemes
get affixed to a root (Oflazer, 1994). At the syntax
level, the unmarked constituent order is SOV, but
constituent order may vary freely as demanded by
the discourse context. Essentially all constituent
orders are possible, especially at the main sen-
tence level, with very minimal formal constraints.
In written text however, the unmarked order is
dominant at both the main sentence and embedded
clause level.
Turkish morphotactics is quite complicated: a
given word form may involve multiple derivations
and the number of word forms one can generate
from a nominal or verbal root is theoretically in-
finite. Derivations in Turkish are very produc-
tive, and the syntactic relations that a word is in-
89
volved in as a dependent or head element, are de-
termined by the inflectional properties of the one
or more (possibly intermediate) derived forms. In
this work, we assume that a Turkish word is rep-
resented as a sequence of inflectional groups (IGs
hereafter), separated by ˆDBs, denoting derivation
boundaries, in the following general form:
root+IG1 +ˆDB+IG2 +ˆDB+··· +ˆDB+IGn.
Here each IGi denotes relevant inflectional fea-
tures including the part-of-speech for the root and
for any of the derived forms. For instance, the de-
rivedmodifiersaˇglamlas¸tırdıˇgımızdaki1
would be represented as:2
saˇglam(strong)+Adj
+ˆDB+Verb+Become
+ˆDB+Verb+Caus+Pos
+ˆDB+Noun+PastPart+A3sg+P3sg+Loc
+ˆDB+Adj+Rel
The five IGs in this are the feature sequences sep-
arated by the ˆDB marker. The first IG shows the
part-of-speech for the root which is its only inflec-
tional feature. The second IG indicates a deriva-
tion into a verb whose semantics is “to become”
the preceding adjective. The third IG indicates
that a causative verb with positive polarity is de-
rived from the previous verb. The fourth IG in-
dicates the derivation of a nominal form, a past
participle, with +Noun as the part-of-speech and
+PastPart, as the minor part-of-speech, with
some additional inflectional features. Finally, the
fifth IG indicates a derivation into a relativizer ad-
jective.
A sentence would then be represented as a se-
quence of the IGs making up the words. When a
word is considered as a sequence of IGs, linguis-
tically, the last IG of a word determines its role
as a dependent, so, syntactic relation links only
emanate from the last IG of a (dependent) word,
and land on one of the IGs of a (head) word on
the right (with minor exceptions), as exemplified
in Figure 2. And again with minor exceptions, the
dependency links between the IGs, when drawn
above the IG sequence, do not cross.3 Figure 3
from Oflazer (2003) shows a dependency tree for
a Turkish sentence laid on top of the words seg-
mented along IG boundaries.
With this view in mind, the dependency rela-
tions that are to be extracted by a parser should be
relations between certain inflectional groups and
1Literally, “(the thing existing) at the time we caused
(something) to become strong”.
2The morphological features other than the obvious part-
of-speech features are: +Become: become verb, +Caus:
causative verb, +PastPart: Derived past participle,
+P3sg: 3sg possessive agreement, +A3sg: 3sg number-
person agreement, +Loc: Locative case, +Pos: Positive Po-
larity, +Rel: Relativizing Modifier.
3Only 2.5% of the dependencies in the Turkish treebank
(Oflazer et al., 2003) actually cross another dependency link.
Figure 2: Dependency Links and IGs
not orthographic words. Since only the word-
final inflectional groups have out-going depen-
dency links to a head, there will be IGs which do
nothave anyoutgoing links (e.g., thefirstIGofthe
word b¨uy¨umesi in Figure 3). We assume that such
IGs are implicitly linked to the next IG, but nei-
ther represent nor extract such relationships with
the parser, as it is the task of the morphological
analyzer to extract those. Thus the parsing mod-
els that we will present in subsequent sections all
aim to extract these surface relations between the
relevant IGs, and in line with this, we will employ
performance measures based on IGs and their re-
lationships, and not on orthographic words.
We use a model of sentence structure as de-
picted inFigure 4. Inthis figure, thetop part repre-
sents thewordsinasentence. Aftermorphological
analysis and morphological disambiguation, each
word is represented with (the sequence of) its in-
flectional groups, shown in the middle of the fig-
ure. The inflectional groups are then reindexed
so that they are the “units” for the purposes of
parsing. The inflectional groups marked with ∗
are those from which a dependency link will em-
anate from, to a head-word to the right. Please
note that the number of such marked inflectional
groups is the same as the number of words in the
sentence, and all of such IGs, (except one corre-
sponding to the distinguished head of the sentence
which will not have any links), will have outgoing
dependency links.
Inthe rest of this paper, wefirst givea very brief
overview a general model of statistical depen-
dency parsing and then introduce three models for
dependency parsing of Turkish. We then present
our results for these models and for some addi-
tional experiments for the best performing model.
We then close with a discussion on the results,
analysis of the errors the parser makes, and con-
clusions.
3 Parser
Statistical dependency parsers first compute the
probabilities of the unit-to-unit dependencies, and
then find the most probable dependency tree T∗
among the set of possible dependency trees. This
90
Bu eski ev+de +ki gül+ün böyle büyü +me+si herkes+i çok etkile+di
Mod
Det
Mod
Subj
Mod
Subj
Obj
Mod
bu
+Det
eski
+Adj
ev
+Noun
+A3sg
+Pnon
+Loc
+Adj gül
+Noun
+A3sg
+Pnon
+Gen
böyle
+Adv
büyü
+Verb
+Noun
+Inf
+A3sg
+P3sg
+Nom
herkes
+Pron
+A3pl
+Pnon
+Acc
çok
+Adv
etkile
+Verb
+Past
+A3sg
This               old             house-at+that-is         rose's            such                     grow +ing              everyone        very      impressed
Such growing of the rose in this old house impressed everyone very much.
+’s indicate morpheme boundaries. The rounded rectangles show the words while the inflectional groups within
the words that have more than 1 IG are emphasized with the dashed rounded rectangles. The inflectional features
of each inflectional group as produced by the morphological analyzer are listed below.
Figure 3: Dependency links in an example Turkish sentence.
w1
d4d4 d15d15 d35d35
IG1
d15d15
IG2
d15d15
··· IG∗g1
d15d15
IG1 IG2 ··· IG∗g1
w2
d1d1 d15d15 d36d36
IG1
d15d15
IG2 ··· IG∗g2
d15d15
IGg1+1 ··· IG∗g1+g2
...
...
wn
d4d4 d15d15 d35d35
IG1 IG2 ··· IG∗gn
d15d15
··· IG∗Υn
Υi =C8ik=1 gk
Figure 4: Sentence Structure
can be formulated as
T∗ = argmax
T
P(T,S)
= argmax
T
n−1productdisplay
i=1
P(dep(wi,wH(i))|S)(1)
where in our case S is a sequence of units (words,
IGs) and T, ranges over possible dependency
trees consisting of left-to-right dependency links
dep(wi,wH(i)) with wH(i) denoting the head unit
to which the dependent unit, wi, is linked to.
The distance between the dependent units plays
an important role in the computation of the depen-
dency probabilities. Collins (1996) employs this
distance ∆i,H(i) in the computation of word-to-
word dependency probabilities
P(dep(wi,wH(i))|S) ≈ (2)
P(link(wi,wH(i))|∆i,H(i))
suggesting that distance is a crucial variable when
deciding whether two words are related, along
with other features such as intervening punctua-
tion. Chung and Rim (2004) propose a different
method and introduce a new probability factor that
takes into account the distance between the depen-
dent and the head. The model in equation 3 takes
into account the contexts that the dependent and
head reside in and the distance between the head
and the dependent.
P(dep(wi,wH(i))|S) ≈ (3)
P(link(wi,wH(i)))|Φi ΦH(i)) ·
P(wi links to some head
H(i) − i away|Φi)
Here Φi represents the context around the depen-
dent wi and ΦH(i), represents the context around
the head word. P(dep(wi,wH(i))|S) is the prob-
ability of the directed dependency relation be-
tween wi and wH(i) in the current sentence, while
P(link(wi,wH(i))|Φi ΦH(i))istheprobability of
seeing asimilardependency (withwi asthedepen-
dent, wH(i) as the head in a similar context) in the
training treebank.
For the parsing models that will be described
below, the relevant statistical parameters needed
have been estimated from the Turkish treebank
(Oflazer et al., 2003). Since this treebank is rel-
atively smaller than the available treebanks for
other languages (e.g., Penn Treebank), we have
91
opted to model the bigram linkage probabilities
in an unlexicalized manner (that is, by just taking
certain morphosyntactic properties into account),
to avoid, to the extent possible, the data sparseness
problem which is especially acute for Turkish. We
have also been encouraged by the success of the
unlexicalized parsers reported recently (Klein and
Manning, 2003; Chung and Rim, 2004).
For parsing, we use a version of the Backward
Beam Search Algorithm (Sekine et al., 2000) de-
veloped for Japanese dependency analysis adapted
to our representations of the morphological struc-
tureofthewords. Thisalgorithm parses asentence
by starting from the end and analyzing it towards
thebeginning. Bymakingtheprojectivity assump-
tion that the relations do not cross, this algorithm
considerably facilitates the analysis.
4 Details of the Parsing Models
In this section we detail three models that we have
experimented with for Turkish. All three models
are unlexicalized and differ either in the units used
for parsing or in the way contexts modeled. In
all three models, we use the probability model in
Equation 3.
4.1 Simplifying IG Tags
Our morphological analyzer produces a rather rich
representation with a multitude of morphosyntac-
tic and morphosemantic features encoded in the
words. However, not all of these features are nec-
essarily relevant in all the tasks that these analyses
can be used in. Further, different subsets of these
features may be relevant depending on the func-
tion of a word. In the models discussed below, we
use a reduced representation of the IGs to “unlex-
icalize” the words:
1. For nominal IGs,4 we use two different tags
depending on whether the IG is used as a de-
pendent or as a head during (different stages
of ) parsing:
• If the IG is used as a dependent, (and,
only word-final IGs can be dependents),
we represent that IG by a reduced tag
consisting of only the case marker, as
that essentially determines the syntactic
function of that IG as a dependent, and
only nominals have cases.
• If the IG is used as a head, then we use
only part-of-speech and the possessive
agreement marker in the reduced tag.
4These are nouns, pronouns, and other derived forms that
inflectwiththesameparadigm asnouns, includinginfinitives,
past and future participles.
2. For adjective IGs with present/past/future
participles minor part-of-speech, we use the
part-of-speech when they are used as depen-
dents and the part-of-speech plus the the pos-
sessive agreement marker when used as a
head.
3. For other IGs, we reduce the IG to just the
part-of-speech.
Such a reduced representation also helps alleviate
the sparse data problem as statistics from many
word forms with only the relevant features are
conflated.
We modeled the second probability term on the
right-hand side of Equation 3 (involving the dis-
tance between the dependent and the head unit) in
the following manner. First, we collected statis-
tics over the treebank sentences, and noted that,
if we count words as units, then 90% of depen-
dency links link to a word that is less than 3 words
away. Similarly, if we count distance in terms of
IGs, then 90% of dependency links link to an IG
that is less than 4 IGs away to the right. Thus we
selected a parameter k = 4 for Models 1 and 3 be-
low, wheredistance ismeasured interms ofwords,
and k = 5 for Model 2where distance is measured
in terms of IGs, as a threshold value at and beyond
which a dependency is considered “distant”. Dur-
ing actual runs,
P(wi links to some head H(i) − i away|Φi)
was computed by interpolating
P1(wi links to some head H(i) − i away|Φi)
estimated from the training corpus, and
P2(wi links to some head H(i) − i away)
the estimated probability for a length of a link
when no contexts are considered, again estimated
from the training corpus. When probabilities are
estimated from the training set, all distances larger
than k are assigned the same probability. If even
after interpolation, the probability is 0, then a very
small value is used. This is a modified version of
the backed-off smoothing used by Collins (1996)
to alleviate sparse data problems. A similar inter-
polation isusedforthefirstcomponent ontheright
hand side of Equation 3 by removing the head and
the dependent contextual information all at once.
4.2 Model 1 – “Unlexicalized” Word-based
Model
In this model, we represent each word by a re-
duced representation of its last IG when used as a
dependent,5 and by concatenation of the reduced
5Remember that other IGs in a word, if any, do not have
any bearing on how this word links to its head word.
92
representation of its IGs when used as a head.
Since a word can be both a dependent and a head
word, the reduced representation to be used is dy-
namically determined during parsing.
Parsing then proceeds with words as units rep-
resented in this manner. Once the parser links
these units, we remap these links back to IGs to
recover the actual IG-to-IG dependencies. We al-
ready know that any outgoing link from a depen-
dent will emanate from the last IG of that word.
For the head word, we assume that the link lands
on the first IG of that word.6
For the contexts, we use the following scheme.
A contextual element on the left is treated as a de-
pendent and is modeled with its last IG, while a
contextual element on the right is represented as
if it were a head using all its IGs. We ignore any
overlaps between contexts in this and the subse-
quent models.
In Figure 5 we show in a table the sample sen-
tence in Figure 3, the morphological analysis for
each word and the reduced tags for representing
the units for the three models. For each model, we
list the tags when the unit is used as a head and
when it is used as a dependent. For model 1, we
use the tags in rows 3 and 4.
4.3 Model 2 - IG-based Model
In this model, we represent each IG with re-
duced representations in the manner above, but
do not concatenate them into a representation for
the word. So our “units” for parsing are IGs.
Theparser directly establishes IG-to-IGlinks from
word-final IGs to some IG to the right. The con-
texts that are used in this model are the IGs to
the left (starting with the last IG of the preceding
word) and the right of the dependent and the head
IG.
The units and the tags we use in this model are
in rows 5 and 6 in the table in Figure 5. Note
that the empty cells in row 4 corresponds to IGs
which can not be syntactic dependents as they are
not word-final.
4.4 Model 3 – IG-based Model with
Word-final IG Contexts
This model is almost exactly like Model 2 above.
The two differences are that (i) for contexts we
only use just the word-final IGs to the left and the
right ignoring any non-word-final IGs in between
(except for the case that the context and the head
overlap, where we use the tag of the head IG in-
6This choice is based on the observation that in the tree-
bank, 85.6% of the dependency links land on the first (and
possibly the only) IG of the head word, while 14.4% of the
dependency links land on an IG other than the first one.
stead of the final IG); and (ii) the distance function
is computed in terms of words. The reason this
model is used is that it is the word final IGs that
determine the syntactic roles of the dependents.
5 Results
Since in this study we are limited to parsing sen-
tences with only left-to-right dependency links7
which do not cross each other, we eliminated the
sentences having such dependencies (even if they
contain a single one) and used a subset of 3398
such sentences in the Turkish Treebank. The gold
standard part-of-speech tags are used in the exper-
iments. The sentences in the corpus ranged be-
tween 2 words to 40 words with an average of
about 8 words;8 90% of the sentences had less
than or equal to 15 words. In terms of IGs, the
sentences comprised 2 to 55 IGs with an average
of 10 IGs per sentence; 90% of the sentences had
less than or equal to 15 IGs. We partitioned this
set into training and test sets in 10 different ways
to obtain results with 10-fold cross-validation.
We implemented three baseline parsers:
1. The first baseline parser links a word-final IG
to the first IG of the next word on the right.
2. The second baseline parser links a word-final
IG to the last IG of the next word on the
right.9
3. The third baseline parser is a deterministic
rule-based parser that links each word-final
IGtoanIGontheright basedontheapproach
of Nivre (2003). The parser uses 23 unlexi-
calized linking rules and a heuristic that links
any non-punctuation word not linked by the
parser to the last IG of the last word as a de-
pendent.
Table 1 shows the results from our experiments
with these baseline parsers and parsers that are
based on the three models above. The three mod-
elshave been experimented withdifferent contexts
around both the dependent unit and the head. In
each row, columns 3 and 4 show the percentage of
IG–IG dependency relations correctly recovered
for all tokens, and just words excluding punctu-
ation from the statistics, while columns 5 and 6
show the percentage of test sentences for which
all dependency relations extracted agree with the
7In 95% of the treebank dependencies, the head is the
right of the dependent.
8This is quite normal; the equivalents of function words
in English are embedded as morphemes (not IGs) into these
words.
9Note that for head words with a single IG, the first two
baselines behave the same.
93
Figure 5: Tags used in the parsing models
relations in the treebank. Each entry presents the
average and the standard error of the results on the
test set, over the 10 iterations of the 10-fold cross-
validation. Our main goal is to improve the per-
centage of correctly determined IG-to-IG depen-
dency relations, shown in the fourth column of the
table. The best results in these experiments are ob-
tained with Model 3 using 1 unit on both sides of
the dependent. Although it is slightly better than
Model 2 with the same context size, the difference
between the means (0.4±0.2) for each 10 iterations
is statistically significant.
Since wehave been using unlexicalized models,
we wanted to test out whether a smaller training
corpus would have a major impact for our current
models. Table 2 shows results for Model 3 with no
context and 1 unit on each side of the dependent,
obtained by using only a 1500 sentence subset of
the original treebank, again using 10-fold cross
validation. Remarkably the reduction in training
set size has a very small impact on the results.
Although all along, we have suggested that de-
termining word-to-word dependency relationships
is not the right approach for evaluating parser per-
formance for Turkish, we have nevertheless per-
formed word-to-word correctness evaluation so
thatcomparison withother wordbased approaches
can be made. In this evaluation, we assume that a
dependency link is correct if we correctly deter-
mine the head word (but not necessarily the cor-
rect IG). Table 3 shows the word based results for
the best cases of the models in Table 1.
We have also tested our parser with a pure word
model where both the dependent and the head are
represented by the concatenation of their IGs, that
is, by their full morphological analysis except the
root. Theresult forthiscase isgiven inthe lastrow
of Table 3. This result is even lower than the rule-
based baseline.10 Forthis model, if weconnect the
10Also lower than Model 1 with no context (79.1±1.1)
dependent to the first IG of the head as we did in
Model 1, the IG-IG accuracy excluding punctua-
tions becomes 69.9±3.1, which is also lower than
baseline 3 (70.5%).
6 Discussions
Our results indicate that all of our models perform
better than the 3 baseline parsers, even when no
contexts around the dependent and head units are
used. We get our best results with Model 3, where
IGs are used as units for parsing and contexts are
comprised ofwordfinalIGs. Thehighest accuracy
in terms of percent of correctly extracted IG-to-IG
relations excluding punctuations (73.5%) was ob-
tained when one word is used as context on both
sides of the the dependent.11 We also noted that
using a smaller treebank to train our models did
not result in a significant reduction in our accu-
racy indicating that the unlexicalized models are
quite effective, but this also may hint that a larger
treebank with unlexicalized modeling may not be
useful for improving link accuracy.
A detailed look at the results from the best per-
forming model shown in in Table 4,12 indicates
that, accuracy decrases with the increasing sen-
tence length. For longer sentences, we should em-
ploymoresophisticated modelspossibly including
lexicalization.
A further analysis of the actual errors made by
the best performing model indicates almost 40%
of the errors are “attachment” problems: the de-
pendent IGs, especially verbal adjuncts and argu-
ments, link to the wrong IG but otherwise with the
samemorphological features asthecorrect one ex-
cept for the root word. This indicates wemay have
to model distance in a more sophisticated way and
11We should also note that early experiments using differ-
ent sets of morphological features that we intuitively thought
should be useful, gave rather low accuracy results.
12These results are significantly higher than the best base-
line (rule based) for all the sentence length categories.
94
Percentage of IG-IG Percentage of Sentences
Relations Correct With ALL Relations Correct
Parsing Model Context Words+Punc Words only Words+Punc Words only
Baseline 1 NA 59.9 ±0.3 63.9 ±0.7 21.4 ±0.6 24.0 ±0.7
Baseline 2 NA 58.3 ±0.2 62.2 ±0.8 20.1 ±0.0 22.6 ±0.6
Baseline 3 NA 69.6 ±0.2 70.5 ±0.8 31.7 ±0.7 36.6 ±0.8
Model 1 None 69.8 ±0.4 71.0 ±1.3 32.7 ±0.6 36.2 ±0.7
(k=4) Dl=1 69.9 ±0.4 71.1 ±1.2 32.9 ±0.5 36.4 ±0.6
Dl=1 Dr=1 71.3 ±0.4 72.5 ±1.2 33.4 ±0.8 36.7 ±0.8
Hl=1 Hr=1 64.7 ±0.4 65.5 ±1.3 25.4 ±0.6 28.7 ±0.8
Both 71.4 ±0.4 72.6 ±1.1 34.2 ±0.7 37.2 ±0.6
Model 2 None 70.5 ±0.3 71.9 ±1.0 32.1 ±0.9 36.3 ±0.9
(k=5) Dl=1 71.3 ±0.3 72.7 ±0.9 33.8 ±0.8 37.4 ±0.7
Dl=1 Dr=1 71.9 ±0.3 73.1 ±0.9 34.8 ±0.7 38.0 ±0.7
Hl=1 Hr=1 57.4 ±0.3 57.6 ±0.7 23.5 ±0.6 25.8 ±0.6
Both 70.9 ±0.3 72.2 ±0.9 34.2 ±0.8 37.2 ±0.9
Model 3 None 71.2 ±0.3 72.6 ±0.9 34.4 ±0.7 38.1 ±0.7
(k=4) Dl=1 71.2 ±0.4 72.6 ±1.1 34.5 ±0.7 38.3 ±0.6
Dl=1 Dr=1 72.3 ±0.3 73.5 ±1.0 35.5 ±0.9 38.7 ±0.9
Hl=1 Hr=1 55.2 ±0.3 55.1 ±0.7 22.0 ±0.6 24.1 ±0.6
Both 71.1 ±0.3 72.4 ±0.9 35.5 ±0.8 38.4 ±0.9
The Context column entries show the context around the dependent and the head unit. Dl=1 and Dr=1 indicate
the use of 1 unit left and the right of the dependent respectively. Hl=1 and Hr=1 indicate the use of 1 unit left and
the right of the head respectively. Both indicates both head and the dependent have 1 unit of context on both sides.
Table 1: Results from parsing with the baseline parsers and statistical parsers based on Models 1-3.
Percentage of IG-IG Percentage of Sentences
Relations Correct With ALL Relations Correct
Parsing Model Context Words+Punc Words only Words+Punc Words only
Model 3 None 71.0 ±0.6 72.2 ±1.5 34.4 ±1.0 38.1 ±1.1
(k=4, 1500 Sentences) Dl=1 Dr=1 71.6 ±0.4 72.6 ±1.1 35.1 ±1.3 38.4 ±1.5
Table 2: Results from using a smaller training corpus.
Percentage of Word-Word
Relations Correct
Parsing Model Context Words only
Baseline 1 NA 72.1 ±0.5
Baseline 2 NA 72.1 ±0.5
Baseline 3 NA 80.3 ±0.7
Model 1 (k=4) Both 80.8 ±0.9
Model 2 (k=5) Dl=1 Dr=1 81.0 ±0.7
Model 3 (k=4) Dl=1 Dr=1 81.2 ±1.0
Pure Word Model None 77.7 ±3.5
Table 3: Results from word-to-word correctness evaluation.
Sentence Length l (IGs) % Accuracy
1 < l ≤ 10 80.2 ±0.5
10 < l ≤ 20 70.1 ±0.4
20 < l ≤ 30 64.6 ±1.0
30 < l 62.7 ±1.3
Table 4: Accuracy over different length sentences.
95
perhaps use alimited lexicalization such asinclud-
ing limited non-morphological information (e.g.,
verb valency) into the tags.
7 Conclusions
We have presented our results from statistical de-
pendency parsing of Turkish with statistical mod-
els trained from the sentences in the Turkish tree-
bank. The dependency relations are between
sub-lexical units that we call inflectional groups
(IGs) and the parser recovers dependency rela-
tions between these IGs. Due to the modest size
of the treebank available to us, we have used
unlexicalized statistical models, representing IGs
by reduced representations of their morphological
properties. For the purposes of this work we have
limited ourselves to sentences with all left-to-right
dependency links that do not cross each other.
Weget our best results (73.5% IG-to-IG link ac-
curacy) using a model where IGs are used as units
for parsing and we use as contexts, word final IGs
of the words before and after the dependent.
Future work involves a more detailed under-
standing of the nature of the errors and see how
limited lexicalization can help, as well as investi-
gation of more sophisticated models such as SVM
or memory-based techniques for correctly identi-
fying dependencies.
8 Acknowledgement
This research was supported in part by a research
grant from TUBITAK (The Scientific and Techni-
cal Research Council ofTurkey) andfrom Istanbul
Technical University.
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