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An Arabic Language Resource for Computational Morphology Based on the Semitic Model Alexis Neme

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An Arabic Language Resource for Computational Morphology Based on the Semitic Model Alexis Neme An Arabic language resource for computational morphology based on the Semitic model Alexis Neme To cite this version: Alexis Neme. An Arabic language resource for computational morphology based on the Semitic model. Computation and Language [cs.CL]. Université Paris-Est, 2020. English. tel-02894816 HAL Id: tel-02894816 https://hal.archives-ouvertes.fr/tel-02894816 Submitted on 22 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. École doctorale MSTIC Mathématiques — Sciences et Techniques de l’Information et de la Communication Thèse de doctorat Spécialité Informatique An Arabic language resource for computational morphology based on the Semitic model Alexis Amid Neme soutenue le 1er juillet 2020 devant le jury composé de Mourad Abbas Président et Examinateur Tita Kyriacopoulou Examinatrice Eric Laporte Directeur de thèse Denis Maurel Rapporteur Alexis Nasr Rapporteur Laboratoire d’informatique Gaspard-Monge UMR 8049 LIGM Contents Presentation .............................................................................................................................................. 3 Publications and applications ................................................................................................................... 4 The Arabic resources in the UNITEX platform .......................................................................................... 5 The traditional Semitic model of Arabic morphology ............................................................................... 6 What should computational morphology keep or drop from the traditional model? ............................. 7 Issues and solutions for Arabic computational morphology .................................................................... 8 Redefining traditional Arabic morphology................................................................................................ 9 Taxonomic approach ............................................................................................................................... 10 FST best practices: paradigmatic approach ............................................................................................ 10 UNITEX adjustments to Semitic morphology .......................................................................................... 12 A full account of diacritics and variations ............................................................................................... 13 Future developments .............................................................................................................................. 13 Perspectives ............................................................................................................................................ 15 Conclusions ............................................................................................................................................. 16 Main publications ................................................................................................................................... 18 Other publications .................................................................................................................................. 18 Bibliography ............................................................................................................................................ 19 Appendix A: Arabic conjugator on http://babelarab.univ-mlv.fr/ ....................................................... 21 Appendix B: Arabic spell checker http://babelarab.univ-mlv.fr/ ........................................................ 24 2 Presentation ‘The need for incorporating linguistic knowledge is a major challenge in Arabic Data-driven MT (Machine Translation). Recent attempts to build data-driven systems to translate from and to Arabic have demonstrated that the complexity of word and syntactic structure in this language prompts the need for integrating some linguistic knowledge and with a minimum cost since the amount of linguistic resources added has consequences for computational complexity and portability’ (Zbib, Soudi, 2012:2). We do agree with this quotation. We are perceiving an advancement lately in software dedicated to Arabic language technology based on statistical or rule-based approaches; better accuracy in Arabic linguistic knowledge will improve the output of such software. The needs expressed by Zbib and Soudi are repeated periodically in papers, conferences, and presentations, and since 2000: “Arabic spell checking is an active area of research since results are not satisfactory.” (Shaalan Kh. et al., 2003) and the state-of-the-art did not improve enough according to the author (Shaalan Kh. et al., 2012). Similar complaints about Hebrew are uttered by Wintner, (2008): “However, when wide coverage morphological grammars are considered, finite-state technology does not scale up well.” Statistical approaches applied to Germanic and Romance languages yield a better output than Arabic. So, the problem of Semitic languages might be not in software development, but elsewhere, mainly in a misconception of lexical resources. Still in 2016, the mainstream projects for Arabic lexicon are based on multi-stem approaches and more specifically on the BAMA (2002) lexicon or on resources derived from it, as the Arabic TreeBank (Maamouri and al, 2004) in Pennsylvania, or the resources for MADA+TOKAN (Habash and al., 2009)1, at Columbia University. “Any formal representation that is not adapted to Semitic morphology will be rejected by the majority of Arabic-speaking linguists. Many computational representations have been proposed based on the Semitic model, others were newly created. However, when linguists use a newly created formalism, they continue to work with the traditional root-and-pattern representation and subsequently, they unfold their descriptions for a specific formalism.” (Neme, 2011). In fact, it is very challenging for a linguist or a computer scientist to update the BAMA lexicon. Actually, a lot of software relies on the BAMA morphological tagging (or SAMA, 2004, its successor), which is complemented in the best case by “a morphological backoff procedure” (Habash and al., 2009). Habash’s team at Columbia University undertook to create their own Arabic resource in the MAGEAD (2005) project based on finite-state technologies, but their last paper consistent with this attempt was published in 2011 (Altantawy and al., 2011), and the later papers of the same team use BAMA. The breakthrough of BAMA (2002) opened a range of possible applications for Arabic. Since then, the needs are expressed periodically for a better morphological analysis tagging, but no team was able to propose a more viable and operational 1 In all metric aspects, the newer MADAMIRA (2014) represents a deterioration of accuracy compared to MADA. 3 solution than BAMA and able to deal with requirements of the Arabic Natural Language Processing, and more particularly of the implementation of the Semitic Model. A natural path for Arabic morphology consists in adopting or adapting both the traditional Semitic model and finite-state technologies. On the one hand, we have to facilitate the linguist’s tasks of lexical encoding by proposing a familiar formalism: the Semitic model for morphology2. On the other hand, computer scientists, in general, point to FSTs as standard devices for inflection; and FSTs have shown their simplicity and efficiency in inflectional morphology for European languages. Nevertheless, there are countless complexities in the implementation of this model with such a technique. This is due to the richness of Arabic morphology and to the actual details of the traditional root-and-pattern model. In fact, there is an opposition between the requirement to be faithful to the essence of the Semitic model, for the sake of lexicon encoders, and the necessity to curb the complexity of its traditional version. Yet, no trade-off has been found. Indeed, we have achieved and created from scratch a lexical resource containing 76,000 lemmatized entries, fully vowelized and manually encoded for inflectional morphology, representing more than 6 million inflected forms based on Semitic morphology and using finite-state technologies. Our resources are comprehensive, straightforward, accurate, and easy to update for a native linguist. The availability of such Arabic linguistic resources3 is a significant advantage for data-driven or rule-based applications. For example, usual utilities for pattern matching typically apply regular expressions on texts; our resource offers more facilities. We are able to describe large classes of forms using simple patterns: for instance, the lexical entry of a particular adjective may locate all its variations, 54 forms partially or fully vowelized, or only the feminine plural ones, for instance. Publications and applications In 2011, we published “A lexicon of Arabic verbs constructed on the basis of Semitic taxonomy and using finite-state transducers”, in which we explore the
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