Building a Treebank for VietnameseDependency Parsing Luong Nguyen Thi Dalat University Lamdong, Vietnam Email:luongnt@dlu.edu.vn Linh Ha My, Hung Nguyen Viet, Huyen Nguyen Thi Minh, Phuo
Trang 1Building a Treebank for Vietnamese
Dependency Parsing
Luong Nguyen Thi
Dalat University Lamdong, Vietnam Email:luongnt@dlu.edu.vn
Linh Ha My, Hung Nguyen Viet, Huyen Nguyen Thi Minh, Phuong Le Hong
VNU University of Science Hanoi, Vietnam Email: halinh.hus@gmail.com, hungnguyenviet@vnu.edu.vn, huyenntm@vnu.edu.vn, phuonglh@vnu.edu.vn
Abstract—The problem of Vietnamese syntactic parsing,
espe-cially constituency parsing, has recently been tackled by several
research groups A common effort of the Vietnamese language
processing community has allowed the creation of VietTreebank,
a reference parsed corpus containing about 10,000 sentences for
the constituency parsing task In this paper, we present our
work to build a reference treebank, based on VietTreebank, for
the dependency parsing task, which has not yet been very well
studied for Vietnamese First we define a dependency label set by
adapting the dependency schema developed by the NLP group at
Stanford university and taking into account the particularities of
Vietnamese grammar Then we propose an algorithm to convert
a constituency treebank to a dependency one The algorithm is
tested on a set of 100 sentences of VietTreebank corpus and
gives very good results Finally, we carry out an experiment on
Vietnamese dependency parsing using MaltParser tool and the
dependency treebank converted from VietTreebank.
I INTRODUCTION
Dependency parsing has been one interesting approach to
syntactic parsing in recent years The basic idea of dependency
parsing is to find the syntactic structure which consists of
lexical items, linked by binary asymmetric relations called
dependencies There have been many studies on dependency
parsing Many tools have been developed to solve this problem
Especially, methods based on machine learning give high
accuracy parsing results on English, Chinese or Swedish
For Vietnamese, most studies centered on constituency
parsing such as [1], [2] The Vietnamese treebank reported
in [2] consists of about 10,000 sentences in Penn treebank
format For dependency parsing, there exists only two works,
one of Nguyễn Lê Minh et al [3] which uses MST parser on
a corpus consisting of 450 sentences, and one of Lê Hồng
Phương et al [4], which uses a lexicalized tree-adjoining
grammar parser trained on a subset of the Vietnamese treebank
In this paper, we report on our work on building a large
corpus for Vietnamese dependency parsing We first develop
algorithms for converting from constituency structure to
de-pendency structure We then use the resulting dede-pendency
tree-bank to train MaltParser - a language-independent dependency
parser [5] and report the parsing results
This paper is organized as follows The next section
in-troduces dependency parsing where basic concepts and some
existing works are given The following section presents the
construction of a Vietnamese dependency treebank Finally,
the last section reports experimental results on Vietnamese dependency parsing with MaltParser
II DEPENDENCYPARSING
A Definition
The dependency parsing of a sentence consists in deter-mining the binary asymmetric relations, called dependencies, between its lexical elements A dependency relation between two tokens can be named to clarify the relationship between them
Dependency structure is determined by the relationship
between the center token (head) and its dependent token (dependent), denoted by an arrow By convention, the root of
the arrow is the head, and the top of the arrow is the dependent
In comparison to constituency structure, dependency structure
is more appropriate to represent syntactic structures of free languages, such as Czech or Turkish
In dependency parsing, each syntactic parse of a sentence can be represented by a dependency graph A dependency graph is a graph where each node is a token of the sentence Arcs (edges) of the graph are used to represent dependency relationship between two nodes and the name of the arc is dependency label between those nodes
For example, consider an English sentence: "Bills on ports and immigration were submitted by Senator Brownback, Re-publican of Kansas" Figure 1 shows its dependency graph
containing 13 nodes corresponding to 13 words and 12 rela-tionships connecting these words The relarela-tionships presented
in the sentence are prep(Bills, on), pobj(on, ports) [6].
By convention, a special node that does not correspond to any token in the sentence is introduced to represent the root
of the dependency graph
Dependency parsing is the problem of constructing the most probable dependency graph for a given input sentence The input of a dependency parser is a tokenized and part-of-speech tagged sentence Most studies on dependency parsing employ machine learning techniques To build a a supervised dependency parser for a language, we need a large dependency treebank of that language
B Related Works
Recently dependency parsing has received the attention
of many research groups There have been many studies
2013 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for the Future (RIVF)
Trang 2Bills
nsubjpass
on
prep
ports
pobj
and
cc
immigration
conj
were
auxpass
by
prep
by
prep
Brownback
pobj
Senator
nn
Republican
appos
of
prep
Kansas
pobj
Fig 1 Dependency graph of an English sentence.
and tools for dependency parsing: MaltParser, StanfordParser,
MSTParser Most dependency parsing tools achieve high
accuracy and are suitable for many languages, such as
En-glish, Chinese, German, Czech The accuracy of a parser
is evaluated using two indices: unlabeled attachment score,
which is the proportion of correct head - ASU, and labeled
attachment score, which is the proportion of correct head and
correct dependency type - ASL
1) MSTParser: MSTParser is developed by Ryan
McDon-ald et al [7] MSTParser has two processes: training and
analysis In training, MSTParser uses on-line algorithms [8]
In analysis, MSTParser uses a graph-based algorithm The
accuracy of MSTParser on a variety of languages is quite high:
ASU = 92.8%, ASL = 90.7% for Japanese, ASU = 91.1%,
ASL= 85.9% for Chinese, ASU = 90.4%, ASL= 87.3% for
German .1
2) Stanford Parser: Stanford Parser is developed by NLP
group at Stanford University Stanford Parser defines 53
de-pendency types for English based on Penn Treebank [6]
The accuracy of the parser is quite high, in particular for
English ASU = 87.2% and ASL = 84.2% This parser has
been extended to parse languages other than English, such as
Chinese, German, French and Arabic.2
3) MaltParser: MaltParser is developed by Johan Hall et al.
MaltParser is the most effective dependency parsing tool, with
high accuracy for more than 20 languages MaltParser has two
processes: training and analysis In training, MaltParser uses
support vector machines algorithm In analysis, MaltParser
uses a transition-based algorithm The accuracy of the tool is
high, for example ASU = 88.1%, ASL = 86.3% for English
and ASU = 88.1%, ASL= 83.4% for German.3
1 http://sourceforge.net/projects/mstparser/
2 http://nlp.stanford.edu/software/lex-parser.shtml
3 http://www.maltparser.org/
For Vietnamese, few works on dependency parsing exist because of the lack of training dependency treebank In [3], MST was used to parse dependency structures in Vietnamese text Experiments conducted on 450 Vietnamese sentences (POS tagged) give an accuracy of ASU = 67.7%, and
of ASL = 63.11% Each dependence is assigned a label
by automatic scoring algorithm in MST No concrete label definition is given In [4], dependencies were determined from derivation trees by TAG parsing Each word in the sentence is represented by a elementary tree Derivation trees were constructed from these elementary trees and converted to dependencies by transforming each derivation operation into a dependency relation with label There were 13 labels divided into 3 types: arg (relationship between a head word and its argument), mod (modification relation between a word and its head word), coord (relationship between two lexical heads of two coordinating phrases within a conjunction)
As we can see, the most important step to develop a dependency parser for Vietnamese is to build a reference dependency treebank The definition of a dependency label set is essential for this task In the next section, we present our work on constructing a Vietnamese dependency treebank III BUILDINGVIETNAMESEDEPENDENCYTREEBANK
To build a dependency treebank for Vietnamese, we first define a dependency scheme specific to this language Then
we design an algorithm to convert the available Vietnamese constituency treebank [2] to a dependency treebank
The orgininal constituency treebank is a corpus containing about 10,000 sentences in Penn treebank format An example
sentence is (S-TTL (NP-SUB (Nc-H Mảnh) (N đất) (PP (E-H của) (NP (N-H đạn) (N-H bom)))) (VP (R không) (V-H còn) (NP-DOB (N-H người) (A nghèo))) ( .))4, where
• S, NP, PP are the labels of phrases and clauses;
• Nc, N, R are the labels of tokens;
• SUB, H, DOB, are the functional syntactic labels of
phrases, clauses or tokens
The converting algorithm has two steps: (1) determining all the dependencies in the sentence and (2) labeling the depen-dency relations The first step is solved by determining the central element (head element) of all grammatical phrases and clauses using head rules The second step is done by using a dependency label set and a rule for labeling dependencies
A Dependency Schema
Different dependency labels represent different types of relationships between pairs of tokens of a sentence Typically, the set of dependency labels depends on a particular language Nevertheless, many languages may share an important subset
of dependency labels
The dependency schema developed by the NLP group at Stanford University defines 53 types of English dependency All of them are binary relations where each dependency defines a relation between the head and its dependent We
4 What used to be the land of bombs was no longer the land of the poor.
Trang 3adapt and extend this schema to build a dependency schema
for Vietnamese which takes into account the particularities of
Vietnamese grammar [9] This schema consists of 48 labels, all
of which are explicitly defined and consistent with Vietnamese
syntax The most common dependency labels are given below:
• vmod: verb modifier, for example vmod(đi, qua) in
(VP (V-H đi) (V qua));
• rmod: adverb modifier, for example rmod(Xa xa, nữa)
in (AP (A-H Xa xa) (R nữa));
• dobj: direct object of a verbal phrase, for example
dobj(còn, người) in (VP (R không) (V-H còn)
(NP-DOB (N-H người) (A nghèo)));
• pobj: direct object of a prepositional phrase, for
exam-ple pobj(bằng, cùi_tay) in (PP-MNR (E-H bằng) (NP
(M hai) (N-H cùi_tay) (A cụt_lủn))).
Mảnh-1 đất-2 của-3 đạn-4 bom-5 không-6 còn-7 người-8 nghèo-9 -10
ROOT-0 nsubj
ncdep
prepc
pobj nn neg dobj amod
punct
Fig 2 An example of dependency parsing in Vietnamese
Figure 2 shows a dependency parse of the sentence "Mảnh
đất của đạn bom không còn người nghèo" In this figure, an
edge from "Mảnh" to "đất" indicates that "đất" is the modifier
of "mảnh" The label of this edge is the relationship name
between them
All dependency relations of this sentence are:
ncdep(Mảnh - 1,đất - 2) prepc(Mảnh - 1, của - 2) nsubj(còn - 7, Mảnh - 1) pobj(của - 3, đạn - 4) nn(đạn - 4, bom - 5) neg(còn - 7, không - 6) Root(ROOT - 0, còn - 7) dobj(còn - 7, người - 8) amod(người - 8, nghèo - 9) punct(còn - 7, - 10)
B Head Rules
In order to determine the head element of each phrase, we
build a head rule table This table constitutes an important part
of our work Our head rules follow that presented in [10]
For example, the rule:
VP → -H;VP;V;A;AP;N;NP;S;.*
can be understood as follows: to find the head of a VP phrase,
we browse from left to right to find the first element marked
as -H; if there is such element, it will be the head of the VP phrase, if not, we find the VP element to be the head; if VP
is not found we find V and so on If there is not any such element, take the first element from the left as head (".*") The following example will describe how to find the head
in a phrase: (VP (R không) (V-H còn) (NP-DOB (N-H người) (A nghèo)) First, we need to find the head rule for VP phrase
in the list of head rules The head rule of VP phrase is:
VP → -H;VP;V;A;AP;N;NP;S;.*
Second, we need to browse from left to right in the head rule for VP phrase to find the first element marked as -H which is
(V-H còn) That means the token "còn" is the head of this VP
phrase
C Conversion Algorithm
The conversion algorithm has two stages In the first stage,
a constituency parse is constructed from the bracket format
of each sentence of the treebank For example, the parsed
sentence (S-TTL (NP-SUB (Nc-H Mảnh) (N đất) (PP (E-H của) (NP (N-H đạn) (N-H bom)))) (VP (R không) (V-H còn) (NP-DOB (N-H người) (A nghèo))) ( .)) has the constituency parse
as shown in Figure 3 In the second stage, the constituency parse is converted to the dependency one This stage has three steps First, find the head of each phrase in the sentence using the head rule table (see Algorithm 1) Second, find a label for each dependency (head, dependent) (see Algorithm 2) Finally, build all the labeled dependencies using a recursive routine calling the two previous steps (see Algorithm 3)
D Results
To evaluate the accuracy of the conversion algorithm, we first select a subset of 100 sentences from the Vietnamese
Trang 4S-STL NP-SUB
Nc-H
Mảnh
N
đất
PP E-H
của
NP N-H đạn
N-H bom
VP R
không
V-H còn
NP-DOB N-H người
A nghèo
.
Fig 3 A constituency parse of a sentence in the Vietnamese treebank.
lstHeadRules: list of head rules
for headRule ∈ lstHeadRules do
if headrule.Phrase=P then
hr ← headRule
break
end if
end for
lstRightHR ← hr.Right
for element ∈ lstElements do
for rightEle ∈ lstRightHR do
if element.Phrase=rightEle or element.Pos=rightEle
then
head ← element
break
end if
end for
end for
treebank and manually annotate them with dependency
rela-tions We then run the conversion algorithm presented above on
these sentences to get dependency parses and compare them to
the manual annotation The result is very good–the unlabeled
attachment score is of 99.6% and the labeled attachment score
is perfect on matched attachments
lstLabels: list of labels
for labelele ∈ lstlabel do
lef t ← GetInf ormation(h, labelele.Left)
right ← GetInf ormation(d, labelele.Right)
center ← GetCenterInf ormation(h, d, labelele.center)
if IsLabel(left, right, center) then
l ← labelele.Label
break
end if
end for
lstHead-Rules: list of head rules; lstLabels: list of dependency labels; dpTree: saved dependency tree
if Root=null then
return
end if
if IsLeaf(Root) then
lstElements ← Word(Root) return FindHeadP(Phrase(Root),lstHeadRules,lstElements)
end if
if AllChildIsLeaf(Root) then
for child ∈ Root do
lstElements ← Word(child)
end for
h ← FindHeadP(Phrase(Root),lstHeadRules,lstElements)
for child ∈ Root do
label ← GetDependencyLabel(h, child, lstLabels) depTree ← (h, child, label)
end for
return h
end if
lstHeadChilds ← null
for child ∈ Root do
lstHeadChilds ← ConverToDP(Phrase(child), lstHeadRules,lstLabels, dpTree)
end for
h ← FindHeadP(Phrase(Root),lstHeadRules, lstHeadChilds)
for headchild ∈ lstHeadChild do
label ← GetDependencyLabel(h, headchild, lstLabels) depTree ← (h, headchild, label)
end for
return h
As an example, from the constituency parse (S-TTL (NP-SUB (Nc-H Mảnh) (N đất) (PP (E-H của) (NP (N-H đạn) (N-H bom)))) (VP (R không) (V-H còn) (NP-DOB (N-H người) (A nghèo))) ( .)), the automatic conversion algorithm produces
the following dependency parse:
Table I shows the percentage of common labels assigned
to dependencies on all the Vietnamese treebank containing of about 10,000 sentences
IV EXPERIMENTS WITHMALTPARSER
In this section, we present parsing experiments on the Vietnamese dependency treebank constructed in the previous section We use MaltParser to train and test dependency
Trang 5TABLE I P ERCENTAGE OF COMMON DEPENDENCY LABELS ON THE
V IETNAMESE TREEBANK
No Label %
1 vmod 9.95
2 rmod 6.36
3 nsubj 5.81
4 dobj 5.7
5 pobj 5.6
6 nn 5.55
7 conj 4.67
parsing models on the treebank using cross-validation 10 data
sets are created for training and testing Each round, 500
sentences are randomly selected as test set and the rest is used
to train MaltParser The configuration of the parser that we
use is as follow:
• Transition system: Arc-Eager
• Parser configuration: Nivre with allowroot=true and
allow_reduce=false
• Feature model: NivreEager.xml
• Learner: liblinear
• Oracle: Arc-Eager
The experimental results are described in Table II
TABLE II D EPENDENCY PARSING ACCURACY WITH M ALT P ARSER
No Test (500 sentences) ASU ASL
2 1001-1500 75.58 68.40
3 2001-2500 72.37 65.12
4 3001-3500 74.16 66.58
5 4001-4500 69.69 63.47
6 5001-5500 74.10 67.42
7 6001-6500 73.49 67.27
8 7001-7500 72.76 65.91
9 8001-8500 69.04 63.16
10 9001-9500 72.82 65.74
Average 73.03 66.35
The average ASU is 73.03% and average ASL is 66.35%
In these experiments, MaltParser was not optimized for
Viet-namese, therefore the accuracy was not high The accuracy
can be improved by fixing some errors on the dependency
treebank such as: determining the wrong root in the sentences
with many clauses, or wrong dependencies of special tokens
The set of guidelines for dependency annotation needs to be
defined more clearly to improve the quality of dependency
identification
V CONCLUSION
There has been several works on constituency parsing
but not many works on dependency parsing for Vietnamese
language as few data exist for training dependency parsers
However, dependency parsing provides more useful
informa-tion in natural language processing field than constituency
parser Our work aims to automatically build Vietnamese
dependency treebank from constituency treebanks which exist more frequently The dependency label set is defined based
on Vietnamese grammar in a way allowing us to compare directly our labels with English dependency labels To do this, the English dependency label set developed by the NLP group
at Stanford University is used as reference
Once the Vietnamese dependency treebank of about 10,000 setences converted from VietTreebank, we have done experi-ments on Vietnamese dependency parsing using MaltParser The evaluation results give 73.03% for the average ASU and 66.35% for the average ASL In a first step, these experiment results help to show some errors in the reference data In the next step, we will revise the corpus and carry out experiments with different parsers to find the best methods for Vietnamese dependency parsing
This work is supported by the VNU research grant QG.12.22
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