言語処理学会 第21回年次大会 発表論文集 (2015年3月)

A Dependency Annotation Scheme for Indonesian
Budi Irmawati∗ Hiroyuki Shindo Yuji Matsumoto
Nara Institute of Science and Technology{budi-i, shindo, matsu}@is.naist.jp

Abstract2This paper proposes a dependency annotation
scheme for Indonesian. Our scheme basically follows
the Stanford typed dependency annotation, however,
we propose some adaptations for morphologicallyrich phenomena in Indonesian, such as affixations,
ellipses, and non-verb clauses. This paper presents
those phenomena and describes how the scheme accommodates the phenomena in deciding dependency
relations between two words. Evaluation of thisscheme by training it on the MST parser is also reported.

Currently, dependency resources for Indonesian language is not publicly available. Even though there
are two works on dependency parsing [2, 3], theyused their internal resources. Moreover, Green et al.
[2] only described, in general, their unlabeled headassignment in seven rules, which the trees have theverb as the root of the sentence. Our annotation is
one step further by considering the non-verb clauses.
McDonald et al. [6] defined universal dependency
annotation for German, English, Swedish, Spanish,French, and Korean. However, Indonesian was notcovered by this annotation.

1

Introduction

3

Indonesian has subject-verb-object (SVO) construction but has relatively free word order in terms ofnoun phrases and verb phrases. Although in generalthey are postmodifier phrases, most position of word
modifier is fixed, relative to the word that is modified
by the modifier; either before or after the modifee.
Kübler et al. [4] stated that dependency grammar is better than phrase structure grammar for
languages with free word order. Moreover, Rozovskaya et al. [7] reported some error correction
tasks and showed that dependency relations improved the tasks. These dependency relations also
worked well in our experiment of detection error
types and error positions of sentences written by second language (L2) learners, in which the POS tags
and language models are not strong enough to detect
preposition, verb, and noun error types. Therefore,
in the absence of a dependency parser, we defineda basic annotation scheme to build a dependency
parser model.
This paper is arranged as follows. In Section 2, wesummarize some previous studies on the dependency
resources of Indonesian and a dependency annotation
scheme. Section 3 describes the annotation scheme
followed by the evaluation method in Section 4. We
explain the results in Section 5, then, we conclude
our work and describe the future plans in the lastsection.
∗ on leave from University of Mataram, Indonesia

Related work

The annotation scheme

The annotation scheme follows the Stanford typeddependency (SD) manual [1] adapted to the language
phenomena of Indonesian that affect dependency annotation. After the data preprocessing, we briefly
discuss the “phenomena” based on [8] in Subsection
3.2 followed by the adaptations and added labels in
Subsections 3.3 and 3.4.

3.1

Data Preprocessing

The dependency annotation scheme is applied on 650
error-corrected learner sentence pairs. Both learnersentences and the corresponding corrections were annotated by a native speaker, but this experiment onlyused the corrected sentences to evaluate the annotation scheme.
Most of the sentences are declarative sentences andsome sentences are imperative sentences. All the sentences have been POS tagged1 , split from clitic, andchunked automatically. Clitics are separated frommain words because they can be a subject, an object, a possessive pronoun, or a determiner.
To decrease the complexity of the phrases, the sentences were chunked using a simple finite automaton (FA) to decide the beginning and the end of thechunk. The FA is not explained here because of limited space, but the example below shows a complex
1 sentences is tagged using Morphind(http://septinalarasati.com/work/morphind/)― 740 ―

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All Rights Reserved.

time phrase converted into the simpler one. In example (1a), the dashed lines above and below the sentence are two possible relations for this time phrase
before chunking, while (1b) shows simpler relation
after chunking.
pobj
depnum(1)a.

nnpobj
pobj

b.

tp

tp

We propose some adaptations for Indonesian dependency annotation scheme because the language phenomena described in 3.2 are not covered in the SDannotation.

tp(2)pada tanggal 9 bulan Juli
prep b-tp i-tp i-tp i-tp
ondate
9 month July‘on 9 July’

3.2

Adaptation to Stanford typed dependency annotation• adapt-1, advcl labels an adverbial clause thatmodifies a VP as in (2a). Since the adjective preceded by dengan ‘with’ is used as an adverb andmodifies a VP, the advcl labels this adjective asin (2b).

pada tanggal 9 bulan Juli
prep noun num noun nounon
date
9 month July

pobj

3.3a. Ia berlari dengan
kecepatan
3sg act.run prep.with noun.speed
tinggiadj.high‘S/he runs in high speed’
advcl
mark

Phenomena in IndonesiansubjThis section describes some phenomena that are notcovered in SD annotation. Some adjectives can beused as an adverb while a clitic has different functions depending on the word it precedes or follows.
Ellipsis is an omission of a word from a sentence if
its presence is unnecessary.
To prevent the relations to be affected by an ellipsis, we take care of the ellipsis of verbs and linking verbs; and also non-verb heads because a verb
is usually needed to become the head of a clause.
This subsection describes adverbs, adjectives, clitics,determiners, non-verb clauses, and copulas.
• Adverb. Some adverbs can be formed from adjectives. Some of this adjectives should be precededby dengan ‘with’.
• Adjective. Some adjectives need to be precededby yang ‘which is’ to keep the naturalness of thesentence.
• Clitic. Indonesian uses front-clitics (ku- [1sg] andkau- [2sg]) as a subject of a clause while end-clitics(-ku [1sg], -mu [2sg], and -nya [3sg]) as a directobject, an indirect object, an object of preposition,a possessive pronoun, or a determiner.
• Determiner. Determiners are used to point
which object is referred to by the writer or to show
the cardinality of the object but Indonesian optionally uses a determiner for a singular object.
Some determiners precede the noun they modify.
• Non-verb clauses. The head of the clause is notalways a verb. It can be a noun or an adjective.
• Copula corresponds to a linking verb in grammarof other languages. A copula occurs optionally between the subject and the predicate in non verbal
clauses. A copula is not obligatory, except whereeither the subject or the predicate is long.

― 741 ―

Ia berlari dengan kecepatan tinggi
b. Ia berlari dengan
cepat
3sg act.run prep.with adj.cepat‘S/he runs quickly’
advcl
mark

Ia berlari dengan cepat• adapt-2, rcmod labels a relative clause thatmodifies an NP. Since an adjective preceded byyang ‘which is’ also modifies an NP, rcmod labels this adjective as in (3).
(3)

lukisanyang
indahnoun.painting which is adj.beautiful‘beautiful painting’
rcmod
ref

lukisan yang indah• adapt-3, mark introduces an adverbial phrase.
Since the adjective in adapt-1 comes after dengan ‘with’ is used as an adverb, mark labels
dengan as in (2b).
• adapt-4, ref introduces a relative clause thatmodifies an NP. yang ‘which is’ precedes the adjective that modifies an NP, so the ref labelsyang as in (3).
• adapt-5, labels all front-clitics as subj and labels end-clitic as dobj if the clitic follows a transitive verb; as pobj if the clitic follows a preposition; or as iobj if the clitic follows a ditransitiveverb. adapt-5 also labels the clitic as det if theclitic is used as a determiner or poss if the clitic
is used as a possessive pronoun.

Copyright(C) 2015 The Association for Natural Language Processing.
All Rights Reserved.

• adapt-6, adds tp to label the words inside thetime chunk.
• adapt-7, cop labels a copula. Copula is set asa modifier of a noun as in (4), so if the copula
is omitted from a sentence, it will not change
other relations in the sentence.
(4)‘S/he is a teacher’
ROOT
det

Ia adalah seorang guru
• adapt-8, agent labels an actor in passive typeII as in (5a) because in passive type-I, agent
labels the actor only if the actor is not precededby the preposition oleh ‘by’ as in (5b). If theactor is preceded by oleh, it labels as pobj asin (5c). The passive voices (type-I and type-II)
are described in detail in [8].
agent

(5)

a.

ROOTprepdetpobjBuku ini untuk anda

3.4

Added Label

After evaluating our adaptation scheme, we foundthat MST parser assigned incorrect heads for theadjectives preceded by yang or dengan because yangcan also be used as a pronoun while dengan is also apreposition. For these reasons, we added two labels
and assigned them to the adjectives of adapt-1 andadapt-2. The added labels are described below.

Ia adalah seorang guru
3sg cop.is det.a noun.teachersubjcop‘This book is for you’saya Nani
jemput1sg Nani act.pick.up• addedLabel-1, padv, “adverb with preposition”,
replaces the advcl label in adapt-1 of (2b) because sometimes parser set the relation in (2b)with prep as in (8).
(8)

preppobj

berlari dengan cepat• addedLabel-2, rpmod, “modifier with relativepronoun”, replaces the rcmod label as (3) ofadapt-2 to differentiate the adjective preceded byyang with an adjective that directly modifies anoun.

agentb.

prepc.

4saya dijemput Nani
1sg pass.pick.up Nani
pobjsaya dijemput
oleh Nani
1sg pass.pick.up prep.by Nani

‘I am picked up by Nani’• adapt-9, instead of as an independent word,
a compound preposition is treated as a chunk.
Therefore, adapt-9 adds pmod to label thewords inside the compound preposition such as
samping for word di samping ‘beside’ as in 6.
(6)

Ia berdiri
di.samping saya
3sg act.stand prep.beside 1sg‘S/he stands beside me’
preppobj
pmod

Ia berdiri di samping saya
• adapt-10, In non-verbal clauses, the head ofnoun clauses, adjective clauses, or quantity
clauses is the noun or the adjective, but the nounin prepositional clauses cannot be chose as thehead of the clauses. In prepositional clause casein (7), the topic is chosen as the head.
(7)

Buku
ini
untuk
anda
noun.book det.this prep.for 2sg

Evaluation

To check the consistency of the annotation schemeand the correctness of the annotation, we run MST
parser [5] on the sentences annotated in three annotation schemes.
The schemes are (1) adaptOnly: scheme with adaptions only without chunking; (2) adapt+addedLabel: scheme with adaptions
and added-label but without chunking; and (3)adapt+addedLabel+chunk: scheme with adaptions
and added-label with chunking. We split the sentences into 550 training sentences and 94 test sentences.
We evaluated the accuracy based on the complexity of the sentences: NoClause, Clauses, NoVerbRoot+Clauses, and Subjectless with the proportion of the sentences shown in Table 1. The NoClause are simple sentences that only have subject,verb, and might have an object and prepositional
phrases; the Clauses are sentences with one or moreclauses including prepositional phrases; the NoVerbRoot+Clauses are sentences that their root is not averb but they might have clauses; and the Subjectless are sentences that do not have subject such as
imperative sentences.

5

Experimental Results

Tables 2, 3, and 4 show the unlabeled accuracy score(UAS) and labeled accuracy score (LAS) of the three― 742 ―

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All Rights Reserved.

Table 1: Sentence categories based on the presenceof clause
Complexities#Sent #TokenAll
94929
NoClause
27202
Clauses35
298
NoVerbRoot+Clauses
28210
Subjectless419
Table 2: adaptOnly scheme
Complexities
All
NoClause
Clauses
NonVerbRoot+Clauses
Subjectless

Accuracy
UAS LAS
0.646 0.576
0.797 0.703
0.638 0.581
0.581 0.5
0.737 0.632

All
NoClause
Clauses
NonVerbRoot+Clauses
Subjectless

Accuracy
UAS LAS
0.788 0.707
0.874 0.8
0.723 0.701
0.725 0.642
0.842 0.842

Complete
UAS LAS
0.357 0.214
0.516 0.370
0.191 0.063
0.357 0.214
0.5
0.5

Complete
UAS LAS
0.394 0.214
0.733 0.467
0.345 0.087
0.167 0.167
0.250.5and long distance clauses.

References

This experiment shows that updating head and label
assignment and adding new labels based on the phenomena of the language improve the parser accuracy.
Moreover, for some complex phrases, chunking also
eased the parser in assigning the in-chunk relations.
We used this annotation scheme to generate aparser model for our grammatical error-correctiontask. For future direction we need to improve theparsing pre-process and polish up the scheme to reduce parser errors and handle interrogative sentences

Complexities

AllNoClause
Clauses
NonVerbRoot+Clauses
Subjectless

Accuracy
UAS LAS
0.812 0.731
0.944 0.877
0.825 0.736
0.841 0.754
0.842 0.789

This study is supported in part by the DGHE, Republic of Indonesia under BPPLN Scholarship Batch
7 fiscal year 2012-2015.

Conclusion

Table 3: adapt+addedLabel scheme

Complexities

Acknowledgments

Complete
UAS LAS0.217 0.130
0.370 0.222
0.104 0.125
0.179 0.063
0.25
0.0

different scenarios. Table 3 shows that added the labels increased the dependency accuracy by 0.13 pointcompared to adaptOnly scheme and Table 4 showsthat sentences previously chunked have better accuracy by 0.02 point compared to the sentences thatwere not chunked, both on UAS and LAS.
The head of adverbial clauses and relative clauses
are mostly a verb, which are labeled with advcl or
rcmod depending on adapt-1 or adapt-2. AfteraddedLabel-1, the preposition dengan ‘with’, which
introduces an adverb to modify a verb, is not labeled
as prep and after addedLabel-2, the rcmod relation becomes more consistent because the adjectives
are labeled with rpmod if they are used as an adjective preceded by yang ‘which’.
However, parser sometimes mistakenly labels aclitic as either a determiner or a possessive pronoun
because both clitics follow a noun. The parser also
mistakenly labels a clitic as iobj for a direct object
because the training data contain very few examples
of a clitic labeled as dobj.

6

Table 4: adapt+addedLabel+chunk scheme[1] M. De Marneffe and C. D. Manning. Stanford Typed Dependencies Manual.
URL
http://nlp. stanford. edu/software/dependencies
manual. pdf, 2008.
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Y. Goldberg, D. Das, K. Ganchev, K. Hall,
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[8] J. N. Sneddon, A. Adelaar, D. D. N., and M. C.
Ewing. Indonesian: A Comprehensive Grammar.
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