Six Fuzzy Morphology Methods for Roles of Combines Standard Sentences Persian language
Subject Areas : Computer EngineeringHaniye Rezaei 1 , Homayun Motameni 2 , Behnam Barzegar 3
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Keywords: Fuzzy System, Bi-gram, difuzzifire, independent roles, dependent roles,
Abstract :
Ability to remove ambiguities is one of the main characteristics of the fuzzy systems in resolving the problems like NLP and morphology. On the other hand, most of the conducted studies in the field of morphology of Farsi language are dealing with analysis of words by means of HMM statistical method. Therefore, this paper has conducted the statistical morphology in the role of words in a sentence in the two sets of independent roles like: “Subject, adverb, possess, possessive, subject, subject header, appositive, conjunct, conjunctive, exclamation and proclaimed” using Fuzzy system. Also, in this Fuzzy system, the Max (Product) Fuzzifier by Bi-gram labeling was used. In addition, regarding the importance of defuzzifier of step one, in all fuzzy systems, for the first time, 6 types of defuzzifiers of ‘Maximum membership, center of gravity, weighted average, mean of maximum, smallest of maximum, largest of maximum, center average’ were implemented and obtained results have shown that the Center of gravity defuzzifier method with the mean of 63.698% had better results in comparison with other defuzzifer methods.
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[1] H. Motameni and A. Peykar, "Morphology of Compounds as Standard Words in Persian through Hidden Markov Model and Fuzzy Method," Journal of Intelligent & Fuzzy Systems, vol. 30, no. 3, pp. 1567-1580, 2016.
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[3] J. Wettig, S. Hiltunen and R. Yangarber, "Hidden Markov Models for induction of morphological structure of natural language," Department of Computer Science,University of Helsinki, Finland, Helsinki, 2010.
[4] S. Naderi Parizi, "Implementation of hidden Markov models associated with the ability to apply the language, grammar, search methods and the applicability of the model," Amirkabir University of Technology, Department of Computer Engineering and Information Technology, Tehran, 2007.
[5] C. C. Aggarwal, Data Mining, Switzerland: Springer International, 2015.
[6] M. Mohseni and B. Minaei-bidgoli, "A Persian Part-Of-Speech Tagger Based on Morphological Analysis," in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta, 2010.
[7] W. Khan, A. Daud, K. Khan, J. A. Nasir, M. Basheri, N. Aljohani, F. S. Alotaibi, Part of speech tagging in urdu: Comparison of machine and deep learning approaches, IEEE Access 7 (2019) 38918–38936.
[8] P. Koehn, Statistical Machine Translation, New York: United States of America by Cambridge University Press, 2010, pp. 181-212.
[9] M. Shrivastava, "Hindi POS Tagger Using Naive Stemming : Harnessing Morphological Information Without Extensive Linguistic Knowledge," in ICON-2008:6th International Conference on Natural Language Processing,Macmillan Publishers., India, 2008.
[10] M. Bahrani, H. Sameti, N. Hafezi and S. Momtazi, "A New Word Clustering Method for Building N-Gram Language Models in Continuous Speech Recognition Systems," in New Frontiers in Applied Artificial Intelligence, 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE, Wroclaw, Poland, 2008.
[11] A. F. Alajmi, E. M. Saad and M. H. Awadalla, "Hidden markov model based Arabic morphological analyzer," International Journal of Computer Engineering Research, vol. 2, no. 2, pp. 28-33, 2011.
[12] D. Dutta, S. Halder, T. Gayen, Intelligent part of speech tagger for hindi, Procedia Computer Science 218 (2023) 604–611
[13] T.-L. Tseng, F. Jiang, Y. Kwon, Hybrid type ii fuzzy system & data mining approach for surface finish, Journal of Computational Design and Engineering 2 (3) (2015) 137–147.
[14] A. Chiche, B. Yitagesu, Part of speech tagging: a systematic review of deep learning and machine learning approaches, Journal of Big Data 9 (1) (2022) 1–25.
[15] D. Modi and N. Nain, "Part-of-Speech Tagging of Hindi Corpus Using Rule-Based Method," Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing, Vols. 10.1007/978-81-322-2638-3, no. 28, pp. 241-247, 2016.
[16] J. S. Rohl, "A note on Backus Naur form," Department of Computer Science, The University, Manchester 13, 2010.
[17] S. Poria, E. Cambria, G. Winterstein and G.-B. Huang, "Sentic patterns: Dependency based rules for concept-level sentiment analysis.," Knowledge-Based Systems., 2014.
[18] M. R. Costa-Juss`a, M. Farr´us, J. B. Mari˜no and J. A. Fonollosa, "Study and comparison of rule-based and statistical catalan-spanish machine translation systems," Computing and Informatics, vol. 31, pp. 245-270, 2012.
[19] A. Alnaied, M. Elbendak, A. Bulbul, An intelligent use of stemmer and morphology analysis for arabic information retrieval, Egyptian Informatics Journal 21 (4) (2020) 209–217.
[20] K. Darwish, "Building a shallow morphological analyzer in one day.," in ACL-02 Workshop on Computational Approaches to Semitic Languages, Philadelphia, PA, 2002.
[21] K. Taghva, R. Elkhoury and J. S Coombs, "Arabic stemming without a root dictionary.," in Information Technology: Coding and Computing.ITCC 2005, 2005.
[22] A. Mohamad, A.-S. Riyad and K. Ghass, "Building an Effective Rule-Based Light Stemmer for Arabic Language to Improve Search Effectiveness.," International Arab Journal of Information Technology (IAJIT), vol. 9, no. 4, pp. 368-372, 2012.
[23] T. Buckwalter, "Buckwalter Arabic Morphological Analyzer.," the Linguistic Data Consortium,, Pennsylvania, 2002.
[24] H. K. Al Ameed, S. O. Al Ketbi, A. A. Al Kaabi, K. S. Al Shebli, N. F. Al Shamsi, N. H. Al Nuaimi and S. S. Al Muhairi, "Arabic light stemmer: anew enhanced approach," in The Second International Conference on Innovations in Information Technology (IIT’05), 2005.
[25] L. Larkey, L. Ballesteros and M. Connel, "Improving Stemming for Arabic Information Retrieval: Light Stemming and Co-occurrence Analysis.," in 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002.
[26] A. El-Hajar, M. Hajar and K. Zreik, "A System for Evaluation of Arabic Root Extraction Methods.," in fifth international Conference on Internet and Web Applications and Services., 2010.
[27] H.Alshalabi,S.Tiun,N.Omar,F.N.AL-Aswadi,K.A.Alezabi,Arabiclight-basedstemmer usingnewrules,JournalofKingSaudUniversity-ComputerandInformationSciences34(9)(2022) 6635–6642
[28] M. F. Kabir, K. Abdullah-Al-Mamun, M. N. Huda, Deep learning based parts of speech tagger for bengali, in: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), IEEE, 2016, pp. 26–29.
[29] K. K. Zin, N. Thein, Hidden markov model with rule based approach for part of speech tagging of myanmar language, in: Proceedings of 3rd International Conference on Communications and Information, 2009, pp. 123–128.
[30] F. Pisceldo, M. Adriani, R. Manurung, Probabilistic part of speech tagging for bahasa indonesia, in: Third international MALINDO workshop, 2009, pp. 1–6.
[31] M. Attia, Y. Samih, A. Elkahky, H. Mubarak, A. Abdelali, K. Darwish, Pos tagging for improving code-switching identification in arabic, in: Proceedings of the Fourth Arabic Natural Language Processing Workshop, 2019, pp. 18–29.
[32] F. Pisceldo, M. Adriani, R. Manurung, Probabilistic part of speech tagging for bahasa indonesia, in: Third international MALINDO workshop, 2009, pp. 1–6.
[33] M. Febryanto, I. Sulyaningsih, A. A. Zhafirah, Analysis of translation techniques and quality of translated terms of mechanical engineering in accredited national journals, Professional Journal of English Education 1 (2021) 116–119.
[34] A. Xv, Russian-english bidirectional machine translation system, in: Proceedings of the Fifth Con- ference on Machine Translation, 2020, pp. 320–325.
[35] H. Aldarmaki, A. Ullah, S. Ram, N. Zaki, Unsupervised automatic speech recognition: A review, Speech Communication 139 (2022) 76–91.
[36] M. Creutz, "Unsupervised segmentation of words using prior distributions of morph length and frequency," in Proc. 41st Meeting of ACL, Sapporo,Japan, 2003.
[37] F. Ahmed and A. Nürnberger, "N-grams Conflation Approach for Arabic," in ACM SIGIR Conference, Amsterdam, 2007.
[38] A. Y. Muaad, G. H. Kumar, J. Hanumanthappa, J. B. Benifa, M. N. Mourya, C. Chola,
M. Pramodha, R. Bhairava, An effective approach for arabic document classification using machine learning, Global Transitions Proceedings 3 (1) (2022) 267–271.
[39] K. Tnaji, K. Bouzoubaa, S. L. Aouragh, A light arabic pos tagger using a hybrid approach, in: Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco, Springer, 2021, pp. 199–208.
[40] M. El-Hadj, A.-S. IA and A.-A. AM, "Arabic Part of Speech Tagging Using the Sentence Structure.," in 2nd international Conference on Arabic Language Resources & Tools, Cairo, 2009.
[41] H. Hassani, Part of speech tagging (post) of a low-resource language using another language (devel- oping a pos-tagged lexicon for kurdish (sorani) using a tagged persian (farsi) corpus), CoRR (2022) abs/2201.12793.
[42] S. Alqrainy, M. Alawairdhi, Towards developing a comprehensive tag set for the arabic language, Journal of Intelligent Systems 30 (1) (2020) 287–296.
[43] H. Motameni, A. Ebrahimnejad, J. Vahidi, et al., Morphology of composition functions in persian sentences through a newly proposed classified fuzzy method and center of gravity defuzzification method, Journal of Intelligent & Fuzzy Systems 36 (6) (2019) 5463–5473.
[44] S. M. Assi and M. Haji Abdolhosseini, "Grammatical tagging of a Farsi Corpus.," International Journal of Corpus Linguistics., vol. 5, no. 1, pp. 69-81, 2000.
[45] M. Bijankhan, J. Sheykhzadegan, M. Bahrani and M. Ghayoomi, "Lessons from Building a Persian Written Corpus: Peykare," Language Resources and Evaluation, vol. 45, pp. 143-164, 2011.
[46] M. Shamsfard, H. Sadat Jafari and M. Ilbe, "STeP-1: A Set of Fundamental Tools for Persian Text Processing.," in LREC 2010, Valletta, Malt, 2010.
[47] H. T.-P., L. K.-Y. and W. S.-L., "Mining linguistic browsing patterns in the world wide web," Soft Computing, vol. 5, pp. 329-336, 2002.
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