3,429 research outputs found

    On the evaluation and improvement of arabic wordnet coverage and usability

    Full text link
    The final publication is available at Springer via http://dx.doi.org/10.1007/s10579-013-9237-0[EN] Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).The work presented in Sect. 2.2 was done in the framework of the bilateral Spain-Morocco AECID-PCI C/026728/09 research project. The research of the two first authors is done in the framework of the PROGRAMME D'URGENCE project (grant no. 03/2010). The research of the third author is done in the framework of WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People, DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project and VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. We would like to thank Manuel Montes-y-Gomez (INAOE-Puebla, Mexico) and Sandra Garcia-Blasco (Bitsnbrain, Spain) for their feedback on the work presented in Sect. 2.4. We would like finally to thank Violetta Cavalli-Sforza (Al Akhawayn University in Ifrane, Morocco) for having reviewed the linguistic level of the entire document.Abouenour, L.; Bouzoubaa, K.; Rosso, P. (2013). On the evaluation and improvement of arabic wordnet coverage and usability. Language Resources and Evaluation. 47(3):891-917. https://doi.org/10.1007/s10579-013-9237-0S891917473Abbès, R., Dichy, J., & Hassoun, M. (2004). The architecture of a standard Arabic lexical database: Some figures, ratios and categories from the DIINAR.1 source program. In Workshop on computational approaches to Arabic script-based languages, Coling 2004. Geneva, Switzerland.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009a). Structure-based evaluation of an Arabic semantic query expansion using the JIRS passage retrieval system. In Proceedings of the workshop on computational approaches to Semitic languages, E-ACL-2009, Athens, Greece, March.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009b). Three-level approach for passage retrieval in Arabic question/answering systems. In Proceedings of the 3rd international conference on Arabic language processing CITALA’09, Rabat, Morocco, May, 2009.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2010a). An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering. Special Issue in the International Journal on Information and Communication Technologies/IEEE. June.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2010b). Using the YAGO ontology as a resource for the enrichment of named entities in Arabic WordNet. In Workshop LR & HLT for semitic languages, LREC’10. Malta. May, 2010.Ahonen-Myka, H. (2002). Discovery of frequent word sequences in text. In Proceedings of the ESF exploratory workshop on pattern detection and discovery (pp. 180–189). London, UK: Springer.Al Khalifa, M., & Rodríguez, H. (2009). Automatically extending NE coverage of Arabic WordNet using Wikipedia. In Proceedings of the 3rd international conference on Arabic language processing CITALA’09, May, Rabat, Morocco.Alotaiby, F., Alkharashi, I., & Foda, S. (2009). Processing large Arabic text corpora: Preliminary analysis and results. In Proceedings of the second international conference on Arabic language resources and tools (pp. 78–82), Cairo, Egypt.Baker, C. F., Fillmore, C. J., & Cronin, B. (2003). The structure of the FrameNet database. International Journal of Lexicography, 16(3), 281–296.Baldwin, T., Pool, P., & Colowick, S. M. (2010). PanLex and LEXTRACT: Translating all words of all languages of the world. In Proceedings of Coling 2010, demonstration volume (pp. 37–40), Beijing.Benajiba, Y., Diab, M., & Rosso, P. (2009). Using language independent and language specific features to enhance Arabic named entity recognition. In IEEE transactions on audio, speech and language processing. Special Issue on Processing Morphologically Rich Languages, 17(5), 2009.Benajiba, Y., Rosso, P., & Lyhyaoui, A. (2007). Implementation of the ArabiQA question answering system’s components. In Proceedings of workshop on Arabic natural language processing, 2nd Information Communication Technologies int. symposium, ICTIS-2007, April 3–5, Fez, Morocco.Benoît, S., & Darja, F. (2008). Building a free French WordNet from multilingual resources. Workshop on Ontolex 2008, LREC’08, June, Marrakech, Morocco.Black, W., Elkateb, S., Rodriguez, H, Alkhalifa, M., Vossen, P., Pease, A., et al. (2006). Introducing the Arabic WordNet project. In Proceedings of the third international WordNet conference. Sojka, Choi: Fellbaum & Vossen (eds).Boudelaa, S., & Gaskell, M. G. (2002). A reexamination of the default system for Arabic plurals. Language and Cognitive Processes, 17, 321–343.Brini, W., Ellouze & M., Hadrich, B. L. (2009a). QASAL: Un système de question-réponse dédié pour les questions factuelles en langue Arabe. In 9th Journées Scientifiques des Jeunes Chercheurs en Génie Electrique et Informatique, Tunisia.Brini, W., Trigui, O., Ellouze, M., Mesfar, S., Hadrich, L., & Rosso, P. (2009b). Factoid and definitional Arabic question answering system. In Post-proceedings of NOOJ-2009, June 8–10, Tozeur, Tunisia.Buscaldi, D., Rosso, P., Gómez, J. M., & Sanchis, E. (2010). Answering questions with an n-gram based passage retrieval engine. Journal of Intelligent Information Systems, 34(2), 113–134.Costa, R. P., & Seco, N. (2008). Hyponymy extraction and Web search behavior analysis based on query reformulation. In Proceedings of the 11th Ibero-American conference on AI: advances in artificial intelligence (pp. 1–10).Denicia-carral, C., Montes-y-Gõmez, M., Villaseñor-pineda, L., & Hernandez, R. G. (2006). A text mining approach for definition question answering. In Proceedings of the 5th international conference on natural language processing, FinTal’2006, Turku, Finland.Diab, M. T. (2004). Feasibility of bootstrapping an Arabic Wordnet leveraging parallel corpora and an English Wordnet. In Proceedings of the Arabic language technologies and resources, NEMLAR, Cairo, Egypt.El Amine, M. A. (2009). Vers une interface pour l’enrichissement des requêtes en arabe dans un système de recherche d’information. In Proceedings of the 2nd conférence internationale sur l’informatique et ses applications (CIIA’09), May 3–4, Saida, Algeria.Elghamry, K. (2008). Using the web in building a corpus-based hypernymy-hyponymy Lexicon with hierarchical structure for Arabic. In Proceedings of the 6th international conference on informatics and systems, INFOS 2008. Cairo, Egypt.Elkateb, S., Black, W., Vossen, P., Farwell, D., Rodríguez, H., Pease, A., et al. (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. MA: MIT Press.García-Blasco, S., Danger, R., & Rosso, P. (2010). Drug–drug interaction detection: A new approach based on maximal frequent sequences. Sociedad Española para el Procesamiento del Lenguaje Natural, SEPLN, 45, 263–266.García-Hernández, R. A. (2007). Algoritmos para el descubrimiento de patrones secuenciales maximales. Ph.D. Thesis, INAOE. September, Mexico.García-Hernández, R. A., Martínez Trinidad, J. F., & Carrasco-ochoa, J. A. (2010). Finding maximal sequential patterns in text document collections and single documents. Informatica, 34(1), 93–101.Goweder, A., & De Roeck, A. (2001). Assessment of a significant Arabic corpus. In Proceedings of the Arabic NLP workshop at ACL/EACL, (pp. 73–79), Toulouse, France.Graff, D. (2007). Arabic Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Graff, D., Kong, J., Chen, K., & Maeda, K. (2007). English Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Hammou, B., Abu-salem, H., Lytinen, S., & Evens, M. (2002). QARAB: A question answering system to support the Arabic language. In Proceedings of the workshop on computational approaches to Semitic languages, ACL, (pp. 55–65), Philadelphia.Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics, COLING ‘92 (vol. 2, pp. 539–545).Kanaan, G., Hammouri, A., Al-Shalabi, R., & Swalha, M. (2009). A new question answering system for the Arabic language. American Journal of Applied Sciences, 6(4), 797–805.Kim, H., Chen, S., & Veale, T. (2006). Analogical reasoning with a synergy of HowNet and WordNet. In Proceedings of GWC’2006, the 3rd global WordNet conference, January, Cheju, Korea.Kipper-Schuler, K. (2006). VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. Thesis.Mohammed, F. A., Nasser, K., & Harb, H. M. (1993). A knowledge-based Arabic question answering system (AQAS). In ACM SIGART bulletin (pp. 21–33).Niles, I., & Pease, A. (2001). Towards a standard upper ontology. In Proceedings of FOIS-2 (pp. 2–9), Ogunquit, Maine.Niles, I., & Pease, A. (2003). Linking lexicons and ontologies: Mapping WordNet to the suggested upper merged ontology. In Proceedings of the 2003 international conference on information and knowledge engineering, Las Vegas, Nevada.Ortega-Mendoza, R. M., Villaseñor-pineda, L., & Montes-y-Gõmez, M. (2007). Using lexical patterns to extract hyponyms from the Web. In Proceedings of the Mexican international conference on artificial intelligence MICAI 2007. November, Aguascalientes, Mexico. Lecture Notes in Artificial Intelligence 4827. Berlin: Springer.Palmer, M., P. Kingsbury, & D. Gildea. (2005). The proposition bank: An annotated corpus of semantic roles. Computational Linguistics, 21. USA: MIT Press.Pantel, P., & Pennacchiotti, M. (2006). Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of conference on computational linguistics association for computational linguistics, (pp. 113–120), Sydney, Australia.Rodriguez, H., Farwell, D., Farreres, J., Bertran, M., Alkhalifa, M., & Martí, A. (2008a). Arabic WordNet: Semi-automatic extensions using Bayesian Inference. In Proceedings of the the 6th conference on language resources and evaluation LREC2008, May, Marrakech, Morocco.Rodriguez, H., Farwell, D., Farreres, J., Bertran, M., Alkhalifa, M., Mart., M., et al. (2008b). Arabic WordNet: Current state and future extensions. In Proceedings of the fourth global WordNet conference, January 22–25, Szeged, Hungary.Sharaf, A. M. (2009). The Qur’an annotation for text mining. First year transfer report. School of Computing, Leeds University. December.Snow, R., Jurafsky, D., & Andrew, Y. N. (2005). Learning syntactic patterns for automatic hypernym discovery. In Lawrence K. Saul et al. (Eds.), Advances in neural information processing systems, 17. Cambridge, MA: MIT Press.Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). YAGO: A core of semantic knowledge unifying WordNet and Wikipedia. In Proceedings of 16th international World Wide Web conference WWW’2007, (pp. 697–706), May, Banff, Alberta, Canada: ACM Press.Tjong Kim Sang, E., & Hofmann, K. (2007). Automatic extraction of Dutch hypernym–hyponym pairs. In Proceedings of CLIN-2006, Leuven, Belgium.Toral, A., Munoz, R., & Monachini, M. (2008). Named entity WordNet. In Proceedings of the Sixth international conference on language resources and evaluation (LREC’08), Marrakech, Morocco.Vossen, P. (Ed.). (1998). EuroWordNet, a multilingual database with lexical semantic networks. The Netherlands: Kluwer.Wagner, A. (2005). Learning thematic role relations for lexical semantic nets. Ph.D. Thesis, University of Tübingen, 2005

    Construction of an ontology for intelligent Arabic QA systems leveraging the Conceptual Graphs representation

    Full text link
    The last decade had known a great interest in Arabic Natural Language Processing (NLP) applications. This interest is due to the prominent importance of this 6th most wide-spread language in the world with more than 350 million native speakers. Currently, some basic Arabic language challenges related to the high inflection and derivation, Part-of-Speech (PoS) tagging, and diacritical ambiguity of Arabic text are practically tamed to a great extent. However, the development of high level and intelligent applications such as Question Answering (QA) systems is still obstructed by the lacks in terms of ontologies and other semantic resources. In this paper, we present the construction of a new Arabic ontology leveraging the contents of Arabic WordNet (AWN) and Arabic VerbNet (AVN). This new resource presents the advantage to combine the high lexical coverage and semantic relations between words existing in AWN together with the formal representation of syntactic and semantic frames corresponding to verbs in AVN. The Conceptual Graphs representation was adopted in the framework of a multi-layer platform dedicated to the development of intelligent and multi-agents systems. The built ontology is used to represent key concepts in questions and documents for further semantic comparison. Experiments conducted in the context of the QA task show a promising coverage with respect to the processed questions and passages. The obtained results also highlight an improvement in the performance of Arabic QA regarding the c@1 measure.The work of the last author was carried out in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie, the DIANA APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Abouenour, L.; Nasri, M.; Bouzoubaa, K.; Kabbaj, A.; Rosso, P. (2014). Construction of an ontology for intelligent Arabic QA systems leveraging the Conceptual Graphs representation. Journal of Intelligent and Fuzzy Systems. 27(6):2869-2881. https://doi.org/10.3233/IFS-141248S2869288127

    Developing a Semantic Question Answering System for E-learning Environments using Linguistic Resources

    Get PDF
    The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand the questions without any doubts in meaning and how to provide the most relevant answers to the questions. In this study, a question-answering system for specific courses has been developed to support the learning environment. The research outcomes indicate that the proposed method helps to solve the problem of ambiguities in meaning through the integration of natural language processing tools and semantic resources that can help to overcome several problems related to the natural language structure. This method also helps improve the capability to understand students’ needs and, consequently, to retrieve the most suitable answers

    Deep learning based Arabic short answer grading in serious games

    Get PDF
    Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research

    Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

    Full text link
    The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.Comment: Journal Pape

    Arabic Educational Neural Network Chatbot

    Get PDF
    Chatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers all questions about educational regulations in the United Arab Emirates

    Ten Ways of Leveraging Ontologies for Rapid Natural Language Processing Customization for Multiple Use Cases in Disjoint Domains

    Get PDF
    With the ever-growing adoption of AI technologies by large enterprises, purely data-driven approaches have dominated the field in the recent years. For a single use case, a development process looks simple: agreeing on an annotation schema, labeling the data, and training the models. As the number of use cases and their complexity increases, the development teams face issues with collective governance of the models, scalability and reusablity of data and models. These issues are widely addressed on the engineering side, but not so much on the knowledge side. Ontologies have been a well-researched approach for capturing knowledge and can be used to augment a data-driven methodology. In this paper, we discuss 10 ways of leveraging ontologies for Natural Language Processing (NLP) and its applications. We use ontologies for rapid customization of a NLP pipeline, ontologyrelated standards to power a rule engine and provide standard output format. We also discuss various use cases for medical, enterprise, financial, legal, and security domains, centered around three NLP-based applications: semantic search, question answering and natural language querying
    • …
    corecore