Abstract
In this paper, we propose a new approach for the Arabic question-answering system. This approach is based on the automatic understanding of Arabic texts to convert them into semantic and logical representations. Our approach is designed to determine the relation of the textual entailment between the logical representations of the question and the text passage in order to select the text passage that contains an answer to the question and find the precise answer. For a logical representation, we referred to a semantic representation. The idea is to convert the Arabic texts into conceptual graphs that allow the modelling of textual information by concepts and relations. From these graphs, we proposed an algorithm that transforms each conceptual graph to a logical representation. Finally, we extracted three features and combined them to determine the textual entailment relation between two logical representations of the question and its passage answer. Our approach has been validated through a question-answering system, called NArQAS. For the evaluation, we used questions and text passages from our corpus of questions-texts, AQA-WebCorp. The performance of our approach has reached an accuracy of 74%.
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Abouenour, L. (2014). Three-levels approach for Arabic question answering systems. Diss: Ecole Mohammadia d’Ingénieurs.
Abouenour, L., Bouzoubaa, K., & Rosso, P. (2012). IDRAAQ: New Arabic question answering system based on query expansion and passage retrieval. In CLEF (Online Working Notes/Labs/Workshop).
Abufardeh, S. & Magel, K. (2008). Software localization: The challenging aspects of Arabic to the localization process (Arabization). In IASTED Proceeding of the Software Engineering SE 2008, Innsbruck, Austria, pp. 275–279.
Akour, M., Abufardeh, S., Magel, K., & Al-Radaideh, Q. (2011). QArabPro: A rule based question answering system for reading comprehension tests in Arabic. American Journal of Applied Sciences, 8(6), 652–661.
AlAgha, I., & Abu-Taha, A. (2015). AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web. International Journal of Computer Applications, 125(6), 19–27.
Al-daimi, K., & Abdel-amir, M. (1994). The syntactic analysis of arabic by machine. Computer Humanities, 28, 29–37.
Al-Khalifa, H., Al-Wabil, A. (2007). The Arabic language and the semantic web: Challenges and opportunities. In: The first international symposium on computers and the Arabic language, November 2007, Riyadh, Saudi Arabia.
Babych, B., & Hartley, A. (2003, April). Improving machine translation quality with automatic named entity recognition. In Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools: Resources and Tools for Building MT (pp. 1–8). Association for Computational Linguistics.
Bakari, W., Bellot, P., & Neji, M. (2016). AQA-WebCorp: Web-based factual questions for Arabic. Procedia Computer Science, 96, 275–284.
Bdour, W. N., & Gharaibeh, N. K. (2013). Development of yes/no Arabic question answering system. International Journal of Artificial Intelligence & Applications (IJAIA). https://doi.org/10.5121/ijaia.2013.410551.
Belguith, L. H., Aloulou, C., & Hamadou, A. B. (2007). MASPAR: De la segmentation à l’analyse syntaxique de textes arabes. In CÉPADUÈS-Editions, editeur, Revue Information Interaction Intelligence I, Vol. 3, pp. 9–36.
Belguith, L., Baccour, L., & Mourad, G. (2005). Segmentation de textes arabes basée sur l’analyse contextuelle des signes de ponctuations et de certaines particules. In Actes de la 12éme Conférence annuelle sur le Traitement Automatique des Langues Naturelles (pp. 451–456).
Ben-Abacha, A. (2012). Recherche de réponses précises à des questions médicales : le système de questions-réponses MEANS, PhD thesis. Universite PARIS-SUD 11 LIMSI-CNRS. JUIN 2012.
Ben-Sghaier, M., Bakari, W. & Neji, M. (2017, December). An Arabic question-answering system combining a semantic and logical representation of texts. International Conference on Intelligent Systems Design and Applications
Brini, W., Ellouze, M., & Hadrich Belguith, L. (2009). QASAL: Un système de question-réponse dédié pour les questions factuelles en langue Arabe. In 9ème Journées Scientifiques des Jeunes Chercheurs en Génie Electrique et Informatique, Tunisia.
Chavan, G., & Gore, S. (2016). Design of the effective question answering system by performing question analysis using the classifier. International Journal of Computer Applications, 139(14), 1–3.
Dao, T. N., & Simpson, T. (2005). Measuring similarity between sentences. WordNet. Net, Technical Report.
Ehrmann, M. (2008). Les entités nommées, de la linguistique au TAL : statut théorique et méthodes de désambiguïsation. PhD thesis, Université Paris 7.
Elkateb, S., Black, W., Vossen, P., Farwell, D., Rodríguez, H., Pease, A. & Alkhalifa, M. (2006). Arabic WordNet and the Challenges of Arabic. In Proceedings of Arabic NLP/MT Conference, London, UK.
Fellbaum, C. (Ed.). (1998). WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.
Habash, N. & Rambow, O. (2005). Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 573–580, June 25–30, Ann Arbor, MI.
Kabadjov, M., Steinberger, J., & Steinberger, R. (2013). Multilingual statistical news summarization. In T. Poibeau, H. Saggion, J. Piskorski, & R. Yangarber (Eds.), Multilingual information extraction and summarization (Vol. 2013, pp. 229–252)., Theory and Applications of Natural Language Processing Berlin, Heidelberg: Springer.
Larkey, L. S., & Connell, M. E. (2001). Arabic information retrieval at UMass in TREC-10. Austin: TREC.
Manning, C. & Jurafsky, D. (2012). StanfordNLP Group Official Website. Retrieved from July 14, 2012, http://nlp.stanford.edu/software/index.shtml. Checked July 14th, 2012.
Metzler, D., & Croft, W. B. (2004). Analysis of statistical question classification for fact-based questions. Journal of Information Retrieval, 8, 481–504.
Mollá, D, van Zaanen, M. & Smith, D. (2006). Named entity recognition for question answering. In Zakerman Covendon, Lowrence and Ingrid, editors, Proceedings of the 2006 Australasian Language Technology Workshop (ALTW 2006), pp. 51–58, Sancta Sophia Collage, Sydney.
Mouelhi, Z. (2008, March). AraSeg: un segmenteur semi-automatique des textes arabes. In JADT 2008 (pp. 867–877). Presses Universitaires de Lyon.
Mourad, G. (2001). Analyse informatique des signes typographiques pour la segmentation de textes et l’extraction automatique de citations: réalisation des applications informatiques: SegATex et CitaRE (Doctoral dissertation, Paris 4).
MuṢṬafa, M., Sayed Ahmed, N., Darwich, M., & Abdallah, A. (2008). Mu’jam al-Wasīṭ. Dār Iḥyā’ al-Turāth al-’Arabī lil-Ṭibā’ah wa-al-Nashr wa-al-Tawzī: Bayrūt.
Nanda, M. (2014). The named entity recognizer framework. International Journal of Innovative Research in Advanced Engineering (IJIRAE). https://doi.org/10.13140/RG.2.2.17432.03844.
Olvera-Lobo, M. D., & Gutiérrez-Artacho, J. (2015). Question answering track evaluation in TREC, CLEF and NTCIR. In New Contributions in Information Systems and Technologies (pp. 13–22). Springer International Publishing.
Payne, S. J., & Reader, W. R. (2006). Constructing structure maps of multiple on-line texts. International Journal of Human Computer Studies, 64, 461–474. https://doi.org/10.1016/j.ijhcs.2005.09.003.
Pudaruth, S., Boodhoo, K., & Goolbudun, L. (2016, March). An intelligent question answering system for ICT. In International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 2895–2899). IEEE.
Sheker, M., Saad, S., Abood, R., & Shakir, M. (2016). Domain-specific ontology-based approach for arabic question answering. Journal of Theoretical and Applied Information Technology, 1083(1), 43.
Sowa John, F. (1984). Conceptual structures: Information processing in mind and machine. Boston: Addison-Wesley Company.
Trigui, O., Belguith, L. H., Rosso, P., Amor, H. B. & Gafsaoui, B. (2012). Arabic QA4MRE at CLEF 2012: Arabic Question Answering for Machine Reading Evaluation. CLEF (Online Working Notes/Labs/Workshop).
Witten, I. H., & Frank, E. (1999). Data mining Practical machine learning tools and techniques with Java implementations. Burlington: Morgan Kaufmann.
Zribi, I., Hammami, S. M. & Belguith, L. H. (2010). L’apport d’une approche hybride pour la reconnaissance des entités nommées en langue arabe. In TALN’ 2010, Montréal, 19–23 juillet 2010 (pp. 19–23).
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Bakari, W., Neji, M. A novel semantic and logical-based approach integrating RTE technique in the Arabic question–answering. Int J Speech Technol 25, 1–17 (2022). https://doi.org/10.1007/s10772-020-09684-0
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DOI: https://doi.org/10.1007/s10772-020-09684-0