Building a treebank for vietnamese depen

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Building a Treebank for Vietnamese Dependency Parsing Luong Nguyen Thi Dalat University Information technology Lamdong, Vietnam Linh Ha My, Hung Nguyen Viet, Huyen Nguyen Thi Minh, Phuong Le Hong VNU University of Science Hanoi, Vietnam Email:,,, Abstract—The problem of Vietnamese syntactic parsing, especially 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 been very well studied for Vietnamese First we define a dependency label set in adapting the dependency schema developed by the NLP group at Stanford university and in 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 the dependency parsing Many tools have been developed to solve this problem Especially, methods based on machine learning give high accuracy parsing results on in English, Chinese, Swedish For Vietnamese, most studies centered on constituency parsing such as [?], [?] The Vietnamese treebank reported in [?] 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 [?] which uses MST parser on a corpus consisting of 450 sentences, and one of Lê Hồng Phương et al [?], which uses a lexicalized tree-adjoining grammar parser trained on a subset of the Vietnamese treebank In this paper, we report our work on building a large corpus for Vietnamese dependency parsing We first develop algorithms for converting from constituency structure to dependency structure We then use the resulting dependency treebank to train and evaluate MaltParser - a language-independent dependency parser [?] and report the parsing results This paper is organized as follows The next section introduces 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 DEPENDENCY PARSING A Definition Syntax is the subject of two research communities consisting of linguists and computer scientists Natural language is the object of study of linguists where formal syntax is one language level to be described Computer scientists develops models and algorithms for computer to analyze formal syntax to build natural language processing applications Dependency syntax is syntactic structures containing lexical items, or tokens, connected by binary asymmetric relations called dependencies 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, Republican of Kansas" Its dependency graph contains 13 nodes corresponding to 13 words and 12 relationships connecting words The relationships presented in the sentence are prep(Bills, on), pobj(on, ports) [?] Also by convention, there is a special node, which does not correspond to any token in the sentence and always represents 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 a dependency parser is a tokenized and part-ofspeech tagged sentence Most studies on dependency parsing employ machine learning techniques To build a supervised submitted auxpass nsubjpass Bills were by prep prep on by pobj cc ports prep and conj pobj immigration Brownback nn Senator appos Republican prep of pobj Kansas Fig Dependency graph of an English sentence dependency parser for a language, we need a large dependency treebank of that language B Related Works Recently dependency parsing has been received the attention of many research groups There have been many studies and softwares on dependency parsing: MaltParser, StanfordParser, MSTParser Most dependency parsing tools achieve high accuracy and suitable for many languages as English, 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 McDonald et al [?] MSTParser has two processes: training and analysis In training, MSTParser uses on-line algorithms [?] 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 2) Stanford Parser: Stanford Parser is developed by NLP group at Stanford University Stanford Parser defines 53 dependency types for English based on Penn Treebank [?] The accuracy of the parser is quite high, in particular for English ASU = 87.2% and ASL = 84.2% This parser have 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 effectively 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 All of the above tools are trained using supervised machine learning algorithms and require a large corpus for concerned languages There does not exist such a dependency corpus for Vietnamese The most important step to develop a dependency parser for Vietnamese is to build a dependency corpus In the next section, we present our work on constructing a Vietnamese dependency corpus III BUILDING VIETNAMESE 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 khơng) (V-H còn) (NP-DOB (N-H người) (A nghèo))) ( .)), 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 We design an algorithm to convert this constituency treebank to a dependency treebank The algorithm has two steps: (1) determining all the dependencies in the sentence and (2) labeling the dependency 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 adapt and extend this schema to build a dependency schema for Vietnamese which takes into account the particularities of Vietnamese grammar [?] 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 khơng) (V-H còn) (NP-DOB (N-H người) (A nghèo))); pobj: direct object of a prepositional phrase, for example pobj(bằng, cùi_tay) in (PP-MNR (E-H bằng) (NP (M hai) (N-H cùi_tay) (A cụt_lủn))) can be understood as follows: to find the head of a sentence S, we browse from left to right to find the first element marked as -H; if there is such element, it will be the root of the sentence, if not, we find the S element to be the head; if S is not found we find VP and so on If there is not any such element, take the first element from the left as head (".*") C Conversion Algorithm Fig An example of dependency parsing in Vietnamese Figure shows a dependency parse which includes the following dependence relations: ncdep(đất - 2, Mảnh - 1) prepc(Mảnh - 1, - 2) nsubj(còn - 7, Mảnh - 1) pobj(của - 3, đạn - 4) nn(đạn - 4, bom - 5) neg(còn - 7, khơng - 6) Root(ROOT - 0, - 7) dobj(còn - 7, người - 8) amod(người - 8, nghèo - 9) punct(còn - 7, - 10) 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 khơng) (V-H còn) (NPDOB (N-H người) (A nghèo))) ( .)) has the constituency parse as shown in Figure In the second stage, the constituency S-STL NP-SUB Nc-H N Mảnh đất 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 [?] S SBAR SQ NP VP AP RP PP QP XP YP MDP WHNP WHAP WHRP WHPP WHXP UCP WHADV WHVP → → → → → → ← → → → → → → → → → → → → → -H;S;VP;AP;NP;.* -H;SBAR;S;VP;AP;NP;.* -H;SQ;VP;AP;NP;.* -H;NP;Nc;Nu;Np;N;P;.* -H;VP;V;A;AP;N;NP;S;.* -H;AP;A;N;S;.* -H;RP;R;T;NP;.* -H;PP;E;VP;SBAR;AP;QP;.* -H;QP;M;.* -H;XP;X;.* -H;YP;Y;.* -H;MDP;T;I;A;P;R;X;.* -H;WHNP;NP;Nc;Nu;Np;N;P;.* -H;WHAP;A;N;V;P;X;.* -H;WHRP;P;E;T;X;.* -H;WHPP;E;P;X;.* -H;XP;X;.* -H;.* -H;R;.* -H;V;.* For example, the rule: S → -H; S; VP; AP; NP; * PP E-H B Head Rules VP NP R V-H khơng N-H N-H đạn bom NP-DOB N-H A người nghèo Fig A constituency parse of a sentence in the Vietnamese treebank 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 routines 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 treebank and manually annotate them with dependency relations 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 As an example, from the constituency parse (S-TTL (NPSUB (Nc-H Mảnh) (N đất) (PP (E-H của) (NP (N-H đạn) (NH bom)))) (VP (R không) (V-H còn) (NP-DOB (N-H người) (A nghèo))) ( .)), the automatic conversion algorithm produces the following dependency parse: Algorithm FindHeadP(P, lstHeadRules, lstElements) Require: P: a phrase; lstElements: list of elements in P; lstHeadRules: list of head rules Ensure: head of P for headRule ∈ lstHeadRules if headrule.Phrase=P then hr ← headRule break end if end for lstRightHR ← hr.Right for element ∈ lstElements for rightEle ∈ lstRightHR if element.Phrase=rightEle or element.Pos=rightEle then head ← element break end if end for end for return head Algorithm GetDependentLabel(h, d, lstLabels) Require: (h, d), where d is a head and d is its dependent; lstLabels: list of labels l Ensure: a dependency label l: h −→ d for labelele ∈ lstlabel left ← GetInformation(h, labelele.Left) right ← GetInformation(d, labelele.Right) center ← GetCenterInformation(h, d, if IsLabel(left,right,center) then l ← labelele.Label break end if end for return l Algorithm ConvertToDP(Root,lstHeadRules,lstLabels,dpTree) Require: Root: root node of the constituency tree; lstHeadRules: list of head rules; lstLabels: list of dependency labels; dpTree: saved dependency tree Ensure: Head of the sentence 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 lstElements ← Word(child) end for h ← FindHeadP(Phrase(Root),lstHeadRules,lstElements) for child ∈ Root label ← GetDependencyLabel(h, child, lstLabels) depTree ← (h, child, label) end for return h end if lstHeadChilds ← null for child ∈ Root lstHeadChilds ← ConverToDP(Phrase(child), lstHeadRules,lstLabels, dpTree) end for h ← FindHeadP(Phrase(Root),lstHeadRules, lstHeadChilds) for headchild ∈ lstHeadChild label ← GetDependencyLabel(h, headchild, lstLabels) depTree ← (h, headchild, label) end for return h TABLE I P ERCENTAGE OF 10 Mảnh đất đạn bom khơng người nghèo Nc N E N N R V N A 1 7 nsubj ncdep prepc pobj nn neg Root dobj amod punct Table I shows the percentage of common labels assigned to dependencies on all the Vietnamese treebank containing of about 10,000 sentences IV EXPERIMENTS WITH MALTPARSER 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 COMMON DEPENDENCY LABELS ON THE TREEBANK No Label % vmod 9.95 rmod 6.36 nsubj 5.81 dobj 5.7 pobj nn 5.55 conj 4.67 V IETNAMESE 5.6 parsing models on the treebank using cross-validation There are 10 data sets for training and testing are created 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 MALTPARSER No Test (500 sentences) ASU ASL 1-500 76.43 70.45 1001-1500 75.58 68.40 2001-2500 72.37 65.12 3001-3500 74.16 66.58 4001-4500 69.69 63.47 5001-5500 74.10 67.42 6001-6500 73.49 67.27 7001-7500 72.76 65.91 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 Vietnamese, 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, 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 have been several works on constituency parsing but not many works on dependency parsing for Vietnamese language as few data exists for training dependency parsers However, dependency parsing provides more useful information in natural language processing than constituency parser Our work aims to build automatically a 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 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 sentences converted from VietTreebank, we have done exeriments 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 REFERENCES [1] L T Hương, P H Quang, and N T Thủy, “Một cách tiếp cận việc tự động phân tích cú pháp văn tiếng việt,” Tạp chí tin học Điều khiển học, vol 15, no 4, 2000 [2] P T Nguyen, L V Xuan, T M H Nguyen, V H Nguyen, and P Le-Hong, “Building a large syntactically-annotated corpus of Vietnamese,” in Proceedings of the 3rd Linguistic Annotation Workshop, ACL-IJCNLP, Singapore, 2009 [3] N L Minh, H T Điệp, and T M Kế, “Nghiên cứu luật hiệu chỉnh kết dùng phương pháp MST phân tích cú pháp phụ thuộc tiếng việt,” in ICT-rda 8, Hanoi, Vietnam, 2008, pp 258–267 [4] P Le-Hong, T M H Nguyen, and R Azim, “Vietnamese parsing with an automatically extracted tree-adjoining grammar,” in Proceedings of the IEEE International Conference in Computer Science: Research, Innovation and Vision of the Future, RIVF, HCMC, Vietnam, 2012 [5] J Nivre, J Hall, J Nilsson, A Chanev, G Eryigit, S Kubler, S Marinov, and E Marsi, “Maltparser: A language-independent system for datadriven dependency parsing,” Natural Language Engineering, vol 13, no 2, pp 95–135, 2007 [6] M.-C de Marneffe, B MacCartney, and C D Manning, “Generating typed dependency parses from phrase structure parses,” in Proceedings of LREC 2006, Genoa, Italy, 2006 [7] R McDonald, K Lerman, and F Pereira, “Multilingual dependency parsing with a two-stage discriminative parser,” in Proceedings of the Tenth Conference on Computational Natural Language Learning, 2006 [8] R McDonald, K Crammer, and F Pereira, “Online large-margin training of dependency parsers,” in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, 2005 [9] Q B Diệp and V T Hoàng, Ngữ pháp Tiếng Việt (Vietnamese Grammar) NXB Giáo dục, Hà Nội, Việt Nam, 1999 [10] P Le-Hong, T M H Nguyen, P T Nguyen, and A Roussanaly, “Automated extraction of tree adjoining grammars from a treebank for Vietnamese,” in Proceedings of The Tenth International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+10), Yale University, New Haven, CT, USA, 2010 ... There have been many studies and softwares on dependency parsing: MaltParser, StanfordParser, MSTParser Most dependency parsing tools achieve high accuracy and suitable for many languages as English,... [?] 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,... German, French and Arabic.2 3) MaltParser: MaltParser is developed by Johan Hall et al MaltParser is the most effectively dependency parsing tool, with high accuracy for more than 20 languages MaltParser
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