کاربست گراف برای تعیین شبکۀ معنایی افعال پایۀ زبان فارسی (مقاله فارسی)

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.