In this paper we present initial results on parsing Arabic using treebank-based parsers and automatic

LFG f-structure annotation methodologies. The Arabic Annotation Algorithm (A3) (Tounsi et al., 2009) exploits the rich functional annotations in the Penn Arabic Treebank (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) to assign LFG f-structure equations to trees. For parsing, we modify Bikel’s (2004) parser to learn ATB functional tags and merge phrasal categories with functional tags in the training data. Functional tags in parser output trees

are then "unmasked" and available to A3 to assign f-structure equations. We evaluate the resulting

f-structures against the DCU250 Arabic gold standard dependency bank (Al-Raheb et al., 2006). Currently we achieve a dependency f-score of 77%

Attia, Mohammed

Tounsi, Lamia

van Genabith, Josef

English

In this paper we present initial results on parsing Arabic using treebank-based parsers and automatic\ud
LFG f-structure annotation methodologies. The Arabic Annotation Algorithm (A3) (Tounsi et al., 2009) exploits the rich functional annotations in the Penn Arabic Treebank (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) to assign LFG f-structure equations to trees. For parsing, we modify Bikel’s (2004) parser to learn ATB functional tags and merge phrasal categories with functional tags in the training data. Functional tags in parser output trees\ud
are then "unmasked" and available to A3 to assign f-structure equations. We evaluate the resulting\ud
f-structures against the DCU250 Arabic gold standard dependency bank (Al-Raheb et al., 2006). Currently we achieve a dependency f-score of 77%

Name not available

PARSING ARABIC USING TREEBANK-BASED LFG RESOURCESLamia Tounsi Mohammed Attia Josef van GenabithNCLT, School of ComputingDublin City UniversityProceedings of the LFG09 ConferenceMiriam Butt and Tracy Holloway King (Editors)2009CSLI Publicationshttp://csli-publications.stanford.edu/583AbstractIn this paper we present initial results on parsing Arabic using treebank-based parsers and au-tomatic LFG f-structure annotation methodologies. The Arabic Annotation Algorithm (A3)(Tounsi et al., 2009) exploits the rich functional annotations in the Penn Arabic Treebank (ATB)(Bies and Maamouri, 2003; Maamouri and Bies, 2004) to assign LFG f-structure equations totrees. For parsing, we modify Bikel’s (2004) parser to learn ATB functional tags and mergephrasal categories with functional tags in the training data. Functional tags in parser output treesare then “unmasked” and available to A3 to assign f-structure equations. We evaluate the result-ing f-structures against the DCU250 Arabic gold standard dependency bank (Al-Raheb et al.,2006). Currently we achieve a dependency f-score of 77%.1 Related WorkArabic parsing systems have been reported in (Ditters, 2001; Zabokrtsky and Smrz, 2003; Othman etal., 2003; Ramsay et Mansour, 2007). (Attia, 2008) gives an overview of an LFG rule-based analysisof Arabic using XLE (Xerox Linguistics Environment). He concentrated on short sentences and usedrobustness techniques to increase the coverage. All of these use hand-crafted grammars, which aretime-consuming to produce and difﬁcult to scale to unrestricted data. More recently, the Penn ArabicTreebank (ATB) has been employed to acquire wide-coverage parsing resources. The best-known Arabicstatistical parser was developed by Bikel (Bikel, 2004). Bikel reports parse quality “far below” Englishand Chinese (Kulick et al., 2006). The main reasons cited were a signiﬁcant number of POS-tag in-consistencies (in the version of the ATB available at the time) and the considerable differences betweenArabic and English sentence structure. (Dieb et al., 2004) and (Habash and Rambow, 2005) presentknowledge- and machine-learning-based methods for tokenisation, basic POS tagging with a reducedtagset and base phrase chunking. Bikel’s parser produces phrase-structure trees (c-structures). The mainobjective of our research is to automatically enrich the output of Bikel’s parser with more abstract and“deep” dependency information (in the form of LFG f-structures), using the Arabic A3 annotation al-gorithm (Tounsi et al., 2009), extending the approach of (Cahill et al., 2004), originally developed forEnglish.2 The Penn Arabic Treebank (ATB)Arabic is a subject pro-drop language. It has relatively free word order: mainly S(ubject) V(erb) andO(bject), with VSO and VOS also possible. Arabic is a highly inﬂectional and cliticizing language.The ATB consists of 23,611 parse-annotated sentences (Bies and Maamouri, 2003; Maamouri and Bies,2004) from Arabic newswire text in Modern Standard Arabic (MSA). The ATB annotation schemeinvolves 497 different POS-tags with morphological information (reduced to 24 basic POS-tags by Bikele.g. NN, NNS, JJ), 22 phrasal tags e.g. NP, VP, PP and 20 functional tags e.g. SBJ, OBJ, TPC (52combined functional tags, as functional tags can stack).3 The Arabic Annotation Algorithm (A3)The Arabic Annotation Algorithm (Tounsi et al., 2009) is constructed adapting and revising the method-ology of (Cahill et al., 2004) for English as follows:1. Automatic extraction of the most frequent rule types from the treebank1.2. Head lexicalisation of ATB trees to identify local heads.3. Default f-structure equations are assigned to ATB functional tags. In addition, lexical macros ex-ploits the rich morphological information provided by the ATB.1With 85% token coverage.5844. Left/right annotation principles for COMPs, XCOMPs, ADJUNCTs, etc2.5. Coordination6. Traces to handle non-local dependencies.(Tounsi et al., 2009) report an f-score of 95% on automatically annotated gold ATB trees against theDCU250 Arabic Dependency Bank.4 Adapting the ParserWe use Bikel’s implementation of Collins’ Model 1 as our c-structure engine (Bikel, 2004). As the A3 of(Tounsi et al., 2009) heavily relies on ATB function tags, we modify the Bikel parser to learn ATB tags.We “mask” ATB function tags in the training data by merging phrasal with function tags and adjust thehead-ﬁnding rules in Bikel’s Arabic language pack accordingly. For example, the functional and phrasaltag NP-OBJ are stuck together as NP OBJ which makes the shallow parser interpret it as one phrasaltag during training and parsing (NP-OBJ⇒ NP OBJ). After parsing, we unmask ATB function tags andmake them available to A3.5 Experiments and Evaluation250 of the 23,611 parse-annotated sentences in ATB were randomly selected as test set (Dieb et al.,2004). The DCU 250 gold standard dependency bank for Arabic (Al-Raheb et al., 2006) is semi-automatically constructed using A3 and manual correction and extension. We use gold-POS-taggedATB text and the lexical morphological information from ATB in the results reported below:Precision Recall F-score70.40 72.38 71.37Table 1: C-structure evaluation (Evalb).Precision Recall F-score74.75 81.07 77.78Table 2: F-structure evaluation.6 Discussion and Further WorkCompared to similar results for English which have a dependency f-score of 87% against DCU 105(Cahill et al., 2002), initial results (dependency f-score of 77%) for Arabic are somewhat disappointing.The most likely reason is the explosion in the size of the phrasal category set with 22 ATB phrasalcategories as opposed to 150 (masked) categories (fusing ATB phrasal and functional tags) to be learntby Bikel’s parser, resulting in substantial data-sparseness. However, the result provides a base-line forwhat, to the best of our knowledge, is the ﬁrst treebank-based LFG parsing approach to Arabic. Inan effort to improve on the baseline presented in this paper, our current experiments use a two-stagearchitecture with a simple probabilistic phrase-structure parser, followed by a machine-learning-basedATB function labeller, to provide input to A3.2Left/right annotation matrices play a smaller role than for English because Arabic is a lot less conﬁgurational and has aricher morphology.585ReferencesY. Al-Raheb, A. Akrout, J. van Genabith, J. Dichy. 2006. DCU 250 Arabic Dependency Bank: An LFG GoldStandard Resource for the Arabic Penn Treebank The Challenge of Arabic for NLP/MT at the British ComputerSociety, UK, pp. 105-116.M. Attia. 2008. Handling Arabic Morphological and Syntactic Ambiguity within the LFG Framework with a Viewto Machine Translation. Ph.D. Thesis. The University of Manchester, Manchester, UK.A. Bies and M. Maamouri. 2003. Penn Arabic Treebank Guidelines URL:http://www.ircs.upenn.edu/arabic/Jan03release/guidelines-TB-1-28-03.pdf.D. Bikel. 2004. Intricacies of Collins’ parsing model Computational Linguistics, 30(4), 2004.A. Cahill, M. McCarthy, J. van Genabith and A.Way. 2002. Automatic Annotation of the Penn-Treebank with LFGF-Structure Information. LREC 2002 workshop on Linguistic Knowledge Acquisition and Representation, pp.8-15.A. Cahill, M. Burke, R. ODonovan, J. van Genabith, A. Way. 2004. Long-Distance Dependency Resolution inAutomatically Acquired Wide-Coverage PCFG-Based LFG Approximations. Meeting of the Association forComputational Linguistics ACL 2004, pp. 319-326.M. Diab, K. Hacioglu, D. Jurafsky. 2004. Automatic tagging of arabic text: From raw text to base phrase chunks.North American Chapter of the Association for Computational Linguistics/Human Language TechnologiesConference (HLT-NAACL04).E. Ditters. 2001. A Formal Grammar for the Description of Sentence Structure in Modern Standard Arabic.Workshop on Arabic Processing: Status and Prospects at ACL/EACL, Toulouse, France.N. Habash, O. Rambow. 2005. Arabic Tokenization, Morphological Analysis, and Part-of-Speech Tagging in OneFell Swoop. In Proceedings of the Conference of American Association for Computational Linguistics ACL2005.S. Kulick S, G. Ryan, M. Mitchell. 2006. Parsing the Arabic Treebank: Analysis and improvements. In Proceed-ings of TLT 2006. Treebanks and Linguistic Theories..M. Maamouri and A. Bies. 2004. Developing an Arabic Treebank: Methods, Guidelines, Procedures, and ToolsWorkshop on Computational Approaches to Arabic Script-based Languages, COLING 2004.E. Othman, K. Shaalan, A. Rafea. 2003. A Chart Parser for Analyzing Modern Standard Arabic Sentence MTSummit IX Workshop on Machine Translation for Semitic Languages: Issues and Approaches, USA.A. Ramsay and H. Mansour. 2007. Towards including prosody in a text-to-speech system for modern standardArabic Computer Speech and Language 22:84-103.L. Tounsi, M. Attia, J. van Genabith. 2009. Automatic Treebank-Based Acquisition of Arabic LFG DependencyStructures European Chapter of the Association for Computational Linguistics, EACL 2009, Workshop Com-putational Approaches to Semitic Languages, pp 45-52.Z. Zabokrtsky, O. Smrz. 2003. Arabic syntactic trees: from constituency to dependency European Chapter of theAssociation for Computational Linguistics EACL 2003, pp. 183-186.586

Parsing Arabic using treebank-based LFG resources

In this paper we present initial results on parsing Arabic using treebank-based parsers and automatic
LFG f-structure annotation methodologies. The Arabic Annotation Algorithm (A3) (Tounsi et al., 2009) exploits the rich functional annotations in the Penn Arabic Treebank (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) to assign LFG f-structure equations to trees. For parsing, we modify Bikel’s (2004) parser to learn ATB functional tags and merge phrasal categories with functional tags in the training data. Functional tags in parser output trees
are then "unmasked" and available to A3 to assign f-structure equations. We evaluate the resulting
f-structures against the DCU250 Arabic gold standard dependency bank (Al-Raheb et al., 2006). Currently we achieve a dependency f-score of 77%

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Parsing Arabic using treebank-based LFG resources

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