کاربست گراف برای تعیین شبکه ی معنایی افعال پایه ی زبان فارسی (مقاله فارسی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری زبان‏شناسی، دانشگاه پیام نور، تهران

2 نویسندۀ مسئول، استاد گروه زبان‏شناسی و زبان‏های خارجی، دانشگاه پیام نور، تهران

3 دانشیار، گروه آموزش زبان فارسی به غیرفارسی‏زبانان، دانشگاه بین‏المللی امام خمینی(ره)

4 استادیار، گروه ریاضی، دانشگاه پیام نور، تهران

چکیده

پژوهش میان­رشته­ای حاضر، در چارچوب کلی زبان‏شناسی شناختی، معناشناسی واژگانی (شناختی) و زبان‏شناسی رایانشی و با استفاده از نظریه­ی گراف به تعیین و تحلیل شبکه­ی معنایی بخشی از افعال زبان فارسی می­پردازد. بدین منظور، 50 فعل پایه­ی پربسامد فارسی از  فهرست­های تأییدشدۀ عبادی و همکاران (1393)، بی­جن­خان و همکاران (1393) و صحرایی و همکاران (1396) انتخاب شدند تا قوی­ترین و پربسامدترین روابط مفهومی و درون­زبانی میان آن‏ها با ارائه­ی مشخصات گراف ترسیم­شده توسط فارسی­زبانان، مشخص گردد. روش پژوهش میدانی است، جامعه­ی آماری آن را افراد فارسی­زبان شاغل به تحصیل در یک مرکز علمی-کاربردی و دانش‏جویان دانشگاه صنعتی شریف، که زبان مادری آن­ها فارسی است، تشکیل می­دهند و حجم نمونه 101 نفر بوده است. ابزار پژوهش پرسش‏نامه‏ا‏ی بومی‏‏سازی‏‏شده شامل افعال مورد بحث است که به آزمون­شوندگان داده شد تا هرگونه ارتباط معنایی میان این افعال را ترسیم نمایند. با استفاده از نرم­افزارهای جاوا و پایتون، گراف داده­های مستخرج از پرسش‌نامه­ها، گراف مادر، ترسیم گردید. یافته­های پژوهش نشان می­دهد الگوی گراف ترسیم­شده برای فارسی­زبانان بازتاب­دهنده­ی روابط مفهومی ترادف، تضاد معکوس، چندمعنایی، رابطۀمعنایی استلزام، شامل روابط "نوعی از" (شمول معنایی) و جزء‏واژگی، و همچنین، روابط باهم­آیی و سببی می­باشد 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 فارسی­زبانانی است که زبان مادری آن­ها نیز فارسی است که می­توان از آن در بهینه­سازی تهیه و تدوین مواد درسی دروس پایه برای فارسی­آموزان غیرفارسی‏زبان و همچنین، استفاده از روش­های نوین آموزشی، نظیر آموزش خوشه­ای افعال، استفاده نمود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Applying Graphs to Determine the Semantic Network of Basic Persian Verbs

نویسندگان [English]

  • Roya jadiri jamshidi 1
  • Belghis Rovshan 2
  • Amirreza Vakiliifard 3
  • Nazli Besharati 4
1 Ph.D student, Department of Linguistics and Foreign Languages, Faculty of Persian Literature and Foreign Languages, Payame Noor University (PNU), Tehran, Iran.
2 Corresponding Author, Professor, Department of Linguistics and Foreign Languages, Faculty of Persian Literature and Foreign Languages, Payame Noor University (PNU), Tehran, Iran.
3 Associate Professor, Department of Teaching Persian to Speakers of other Languages, Faculty of Literature and Humanity Sciences, Imam Khomeini International University, Qazvin, Iran.
4 Assistant 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
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دوره 11، شماره 2 - شماره پیاپی 24
ویژه نامه CALL»فناوری در آموزش/یادگیری زبان فارسی و سایر زبانها»
مهر 1401
صفحه 189-220
  • تاریخ دریافت: 26 مهر 1401
  • تاریخ بازنگری: 19 بهمن 1401
  • تاریخ پذیرش: 06 اسفند 1401
  • تاریخ اولین انتشار: 06 اسفند 1401