اخبار و اعلانات
آمار
| تعداد نشریات | 20 |
| تعداد شماره‌ها | 360 |
| تعداد مقالات | 2,962 |
| تعداد مشاهده مقاله | 3,779,232 |
| تعداد دریافت فایل اصل مقاله | 2,553,980 |
کاربست گراف برای تعیین شبکۀ معنایی افعال پایۀ زبان فارسی (مقاله فارسی) | ||
| پژوهش نامه آموزش زبان فارسی به غیر فارسی زبانان | ||
| مقاله 11، دوره 11، (2)- ویژه نامه - شماره پیاپی 24، مهر 1401، صفحه 191-222 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.30479/jtpsol.2023.17975.1615 | ||
| نویسندگان | ||
| رویا جدیری جمشیدی1؛ بلقیس روشن* 2؛ امیررضا وکیلی فرد3؛ نازلی بشارتی4 | ||
| 1دانشجوی دکتری زبانشناسی، دانشگاه پیام نور، تهران | ||
| 2نویسندۀ مسئول، استاد گروه زبانشناسی و زبانهای خارجی، دانشگاه پیام نور، تهران | ||
| 3دانشیار، گروه آموزش زبان فارسی به غیرفارسی زبانان، دانشگاه بین المللی امام خمینی(ره) | ||
| 4استادیار، گروه ریاضی، دانشگاه پیام نور، تهران | ||
| تاریخ دریافت: 26 مهر 1401، تاریخ بازنگری: 19 بهمن 1401، تاریخ پذیرش: 06 اسفند 1401 | ||
| چکیده | ||
| پژوهش میان رشته ای حاضر، در چارچوب کلی زبانشناسی شناختی، معناشناسی واژگانی (شناختی) و زبانشناسی رایانشی و با استفاده از نظریه ی گراف به تعیین و تحلیل شبکه ی معنایی بخشی از افعال زبان فارسی می پردازد. بدین منظور، 50 فعل پایه ی پربسامد فارسی از فهرست های تأییدشدۀ عبادی و همکاران (1393)، بیجن خان و همکاران (1393) و صحرایی و همکاران (1396) انتخاب شدند تا قویترین و پربسامدترین روابط مفهومی و درون زبانی میان آنها با ارائه ی مشخصات گراف ترسیم شده توسط فارسی زبانان، مشخص گردد. روش پژوهش میدانی است، جامعه ی آماری آن را افراد فارسی زبان شاغل به تحصیل در یک مرکز علمی-کاربردی و دانشجویان دانشگاه صنعتی شریف، که زبان مادری آنها فارسی است، تشکیل می دهند و حجم نمونه 101 نفر بوده است. ابزار پژوهش پرسش‌نامه ای بومی سازی شده شامل افعال مورد بحث است که به آزمونشوندگان داده شد تا هرگونه ارتباط معنایی میان این افعال را ترسیم نمایند. با استفاده از نرم افزارهای جاوا و پایتون، گراف دادههای مستخرج از پرسش‌نامه ها، گراف مادر، ترسیم گردید. یافته های پژوهش نشان میدهد الگوی گراف ترسیم شده برای فارسی زبانان بازتاب دهندهی روابط مفهومی ترادف، تضاد معکوس، چندمعنایی، رابطۀمعنایی استلزام، شامل روابط "نوعی از" (شمول معنایی) و جزءواژگی، و همچنین، روابط باهمآیی و سببی می باشد که از این میان، باهمآیی (29.61درصد)، استلزام (85/23درصد) و تضاد (16.71 درصد) بیشترین فراوانی را دارند. دستاورد این پژوهش شناسایی شبکۀمعنایی این افعال در واژگان ذهنی فارسی زبانانی است که زبان مادری آنها نیز فارسی است که میتوان از آن در بهینه سازی تهیه و تدوین مواد درسی دروس پایه برای فارسی آموزان غیرفارسی زبان و همچنین، استفاده از روشهای نوین آموزشی، نظیر آموزش خوشه ای افعال، استفاده نمود. | ||
| کلیدواژه‌ها | ||
| زبانشناسی شناختی؛ معنی شناسی واژگانی (شناختی)؛ شبکۀ معنایی؛ نظریۀگراف؛ تناوب های نحوی دوگانه؛ آموزش زبان فارسی | ||
| عنوان مقاله [English] | ||
| Applying Graphs to Determine the Semantic Network of Basic Persian Verbs | ||
| نویسندگان [English] | ||
| Roya jadiri jamshidi1؛ Belghis Rovshan2؛ Amirreza Vakiliifard3؛ Nazli Besharati4 | ||
| 1Ph.D student, Department of Linguistics and Foreign Languages, Faculty of Persian Literature and Foreign Languages, Payame Noor University (PNU), Tehran, Iran. | ||
| 2Corresponding Author, Professor, Department of Linguistics and Foreign Languages, Faculty of Persian Literature and Foreign Languages, Payame Noor University (PNU), Tehran, Iran. | ||
| 3Associate Professor, Department of Teaching Persian to Speakers of other Languages, Faculty of Literature and Humanity Sciences, Imam Khomeini International University, Qazvin, Iran. | ||
| 4Assistant Professor, Department of Mathematics, Faculty of Basic Sciences, Payame Noor University (PNU), Tehran, Iran. | ||
| چکیده [English] | ||
| The current interdisciplinary research tries to determine and analyze the semantic network of a part of Persian verbs, by applying graph theory, in the general framework of cognitive linguistics, (cognitive) lexical semantics and computational linguistics.To do this50basic Persian verbs of high frequency were selected from the verified lists of Ebadi etal.(2014),Bijankhan etal.(2014)and Sahraee etal.(2017.The research objective was to identify the strongest and most frequent sense and intra-lingual relations between these verbs by means of the characteristics of the graph drawn by Persian speakers whose mother tongue was also Persian.The statistical population of this field study consisted of Persian-speaking students studying in a ScientificApplied Center as well as those of Sharif University of Technology, and the research sample consisted of101examinees.The research instrument was a nativized questionnaire(including the verbs)which was given to the examinees and they were asked to draw any possible semantic relation between these verbs.By applying Java and Python soft wares, the graph of the extracted data from the questionnaires, the mother(social)graph, was drawn.The findings reveal that the graph’s pattern represents sense relations of synonymy, reverse antonymy, polysemy, entailment (including troponymy (hyponymy) and meronymy as well as causative relation and collocation.From among these, collocation(29.61%)entailment(23.85%)and reverses(16.71%)are themost frequent semantic relations.The achievement of this research is the identification of the semantic network oftheverbs in question in the mental lexicon of thePersian-speakers examinees.Thisnot only canbe used in preparing and compiling better materials for basic lessons of non-Persianspeakers who are learning Persian, but also in applying new educationalmethods suchas teaching verbclusters. Extended Abstract: The current interdisciplinary, field study tries to determine and analyze the semantic network of some basic Persian verbs, by applying graph theory, in the general framework of cognitive linguistics, (cognitive) lexical semantics, and computational linguistics. Carroll (2008: 110) believes that the structure of the lexicon is composed of semantic networks with interconnected components. These components are concepts, here the verbs, that are connected to each other through various relationships. Saeed (2003: 63), while discussing the semantic relations hold between lexical items, maintains that “there are a number of different types of lexical relations” and “A particular lexeme may be simultaneously in a number of these relations.” So, the research objective is to identify the strongest and most frequent lexical (sense) and intralingual relations between 50 basic Persian verbs in the mental lexicon of Persian speakers whose mother tongue, as well as whose mothers’ mother tongue, is Persian by availing from graphs and computer science. To meet this end, while having in mind the importance of frequency mentioned by different researchers, such as Schur (2007) and Bijankhan et al. (2014), 50 basic verbs of high-frequency were randomly selected, by a consolidated and comparative method, from the verified lists of Ebadi et al. (2014), Bijankhan et al. (2014) and Sahraee et al. (2017) and a new list was formed. Then the verbs were included in Schur’s (2007) questionnaire after it was nativized. Since the current research is mainly based on the cognitive approach, the clear role of examinees in semantic encoding and decoding was of particular importance, therefore, the data were collected from their questionnaires. Persian-speaking students studying in a Scientific-Applied Center as well as those of the Sharif University of Applied Science and Technology in Tehran made the research population. The research sample consisted of 137 students and according to the table of Morgan and Karjesi (1985), the number of approved questionnaires for these examinees was 101. Therefore, 101 questionnaires were distributed among the examinees. The questionnaire consisted of two parts. The first part included the personal information of the examinees (consisting of their age, education, gender, and mother tongue) and the second part included a two-column table. In the first column, a verb from the new list was presented, and the examinees were asked to write in the second column all the verbs in the list that had a semantic relation, collocation, or similarity with this verb. It was also explained to them that they could use each verb several times. Besides, an audio file was presented to them which explained how to respond correctly. From among the questionnaires, 16 were excluded because the examinees had used verbs outside the list for semantic relations. From the remaining 84 questionnaires, regarding the viewpoints of Guida and Lancey (2007), those questionnaires were selected that identified more than 75% of the verbs as related to each other. That is, the questionnaires of the examinees who had only twelve isolated vertices (or more) were deleted. Finally, 79 standard questionnaires remained which were reviewed and analyzed. To indicate the pattern of semantic relations between the verbs, the authors used graphs. A graph is a set including two components: vertices and edges. Vertices are members of the set that are connected by defined relations or edges. By applying Java and Python soft wares, a social graph (a graph in which each semantic relation/connection is drawn by at least one examinee) and shared graphs (graphs that show which percent of the examinees have a semantic relation/connection between two or more verbs) were drawn and the features of the social graph, high-frequency sense and intralingual relations, the number of drawn edges and semantic clusters were determined. The findings reveal that the social graph pattern represents sense relations of synonymy, reverse antonymy, polysemy, entailment (including troponymy (hyponymy) and meronymy) as well as causative relation and collocation. From among these, collocation (29.61%), entailment (23.85%), and reverses (16.71%) are the most frequent semantic relations. In this graph, the verbs to do, to understand, to go and to move have the highest degree of vertices, and the verbs to reach, to deliver, to return, and to come have the highest average size of the semantic cluster component. The finding of this research is the identification of the semantic network of the basic verbs in the mental lexicon of the Persian-speakers’ examinees. This not only can be used in preparing and compiling proper materials for basic lessons of non-Persian speakers who are learning Persian, but also in applying new educational methods such as teaching verb clusters. Moreover, identifying similarities and differences between the graphs of sense and intralingual relations of bilingual and foreign Persian learners and comparing them with the above-mentioned social graph could be a great subject for future studies. | ||
| کلیدواژه‌ها [English] | ||
| Cognitive linguistics, (cognitive) Lexical semantics, semantic network, graph theory | ||
| مراجع | ||
|
بشارتی، نازلی و محمودی، اکرم. (1394). نظریه گراف و کاربردهای آن. تهران: انتشارات دانشگاه پیام نور.
بیجنخان، محمود؛ نصری، عباس و شهره جلایی (1393)، نقش واژگان بسامدی در ارزیابی مهارت واژگانی فارسیآموزان، پژوهشنامهی آموزش زبان فارسی به غیرفارسیزبانان، سال سوم، شمارهی سوم (پیاپی7)، 25-45.
روشن، بلقیس (1377). معناشناسی واژگانی: طبقهبندی افعال فارسی. رسالهی دکتری زبانشناسی همگانی، دانشکدۀادبیات و علوم انسانی. تهران: دانشگاه تهران.
صحرایی، رضامراد؛ طالبی، مروارید و امیرحسین مجیری (1396)، مقایسۀواژههای پایۀزبان فارسی در شش پژوهش، پژوهشنامهی آموزش زبان فارسی به غیرفارسیزبانان، سال ششم، شمارهی اول (پیاپی 13)، 134-115.
عبادی، سامان؛ وکیلیفرد، امیررضا و خسرو بهراملو (1396)، تدوین فهرست واژگان پایه برای زبان فارسی: رویکردی تلفیقی، پژوهشنامهی آموزش زبان فارسی به غیرفارسیزبانان، سال سوم، شمارهی سوم (پیاپی8)، 4-23.
هماوند، زکی (1400). مکاتب نوین زبانشناسی. ترجمۀسهند الهامی. تهران: نشر مرکز.
References:
Aitchison, J. (1987). Words in the Mind: An Introduction to the Mental Lexicon. Oxford: Blackwell Publishing.
Besharati, N. & Mahmoodi, A. (2015). Graph Theory with Applications. PNU Press, Tehran, Iran. [in Persian].
Bijankhan, M.; Nasri, A. & Jalaei, S. (2014). The Role of Frequency Lexicon in Assessing Lexical Proficiency of Persian Language Learners. Journal of Teaching Persian to Speakers of Other Languages (JTPSOL), Vol. 3, 7 (TOME 7), 25-45. [in Persian].
Carroll, D. (2008). Psychology of Language, (5th ed.). Toronto: Thomson Wadsworth.
Cruse, D.A. (1986). Lexical Semantics. Cambridge: Cambridge University Press.
Dorow, B. (2006). A Graph Model for Words and their Meanings, PhD Dissertation. Stuttgart: University of Stuttgart.
Ebadi, S.; Vakilifad, A. & Bahramlu, Kh. (2014). Developing a General Service Wordlist for Persian Language: An Integrated Approach. Journal of Teaching Persian to Speakers of Other Languages (JTPSOL), Vol. 3, 3 (TOME 8), 4-23,[in Persian].
Evans, V. & Green, M. (2006). Cognitive Linguistics: An introduction. Edinburgh: Edinburgh University Press Ltd.
Fellbaum, Ch. (1990). English verbs as a semantic net. International Journal of Lexicography, 3, 278-301.
Fellbaum, Ch. (1998). A semantic network of English: The mother of all WordNets. Computers and the Humanities, Vol. 32, 209-220.
Ferrer i Cancho, R. & Sole, R.V. (2001). The small world of human language. The Royal Society B: Biological Sciences 268. London: The Royal Society.
Field, J. (2003). Psycholinguistics: A resource book for students. London: Routledge
Gentner, D. & France, I.M. (1988). ‘The Verb Mutability Effect: Studies of the Combinatorial Semantics of Nouns and Verbs.’ In S. Small; G. Cottrell and M. Tanenhaus. (Eds.). Lexical Ambiguity Resolution. Los Altos, Calif.: Morgan Kaufmann.
Guida, A. & Lenci, A. (2007). Semantic properties of word associations of Italian verbs. Rivista di Linguistica. 19, 2, 293-326
Hamawand, Z. (2021). Modern Schools of Linguistic Thought, Markaz Press, Tehran, Iran [in Persian].
Korhonen, A. & T. Briscoe: 2004, ‘Extended Lexical-Semantic Classification of English Verbs.’ In Proceedings of the HLT/NAACL Workshop on Computational Lexical Semantics. Boston, MA.
Levin, B. (1993). English Verb Classes and Alternations: A Preliminary Investigation. Chicago: The University of Chicago Press.
Lyons, J. (1977). Semantics. 2 Vols. New York: Cambridge University Press.
Miller, G.A. & Fellbaum, Ch. (1991). Semantic Networks of English. Cognition, Vol. 41, 1-3, pp. 197-229.
Miller, A.G.; Richard Beckwith, R.; Fellbaum, C.; Gross, D. & Miller, K. (1991). Introduction to WordNet: An On-line Lexical Database. International Journal of LexicographyVol 3, 4, pp 235-244. (Revised: August 1993).
Motter, A.E.; de Moura, S.P.A.; Lai, C.Y. & Dasgupta, P. (2002). Topology of the conceptual network of language. Phys. Rev. E 65, 065102(R).
Pinker, S. (1989). Learnability and Cognition: The Acquisition of Argument Structure. Cambridge, Mass.: MIT Press.
Quillian, M.R. (1968). Semantic memory. In M. Minsky (Ed.), Semantic Information Processing. Cambridge, MA: MIT Press.
Rovshan, B. (1998). Lexical Semantics: Classification of the Persian Verbs. PhD dissertation in General Linguistics. Tehran: Tehran University. [in Persian].
Saeed, John I. (2003). Semantics, (2nd ed.). UK: Blackwell Publishing.
Sahraee, R.; Talebi, M. & Mojiri, A.H. (2017). Basic Words in Persian: A Comparison of Six Studies. Journal of Teaching Persian to Speakers of Other Languages (JTPSOL), Vol. 6, 1 (TOME 13). [in Persian].
Schur, E. (2007). Insights into the structure of L1 and L2 vocabulary networks:Intimations of small worlds. In H. Daller, J. Milton and Treffers-daller (Eds.), Modelling and Assessing Vocabulary Knowledge, (pp. 182-203). Cambridge: Cambridge University Press.
Sigman, M. & Cecchi, G.A. (2002). Global organization of the lexicon. Proceedings of the National Academy of Sciences of the United States of America. Feb 2002, 99 (3):1742-1747
Vossen, P. (2002). EuroWordNet: General Document. Netherlands: University of Amsterdam. Version 3, Final, July 1, 2002. http://hdl.handle.net/1871/11116. | ||
|
آمار تعداد مشاهده مقاله: 257 تعداد دریافت فایل اصل مقاله: 313 |
||