Application of Graph for the Semantic Network of Non-Iranian Learners of Persian

This study, grounded in Cognitive Linguistics and Cognitive Lexical Semantics, uses a graph-theoretical approach to analyze the semantic network of high-frequency Persian verbs as understood by non-native learners. It builds on 50 foundational Persian verbs from Jamshidi et al. (2022) and involves 101 non-native Persian speakers from Imam Khomeini International University. Participants, whose native language is not Persian, used a culturally adapted version of Schur’s (2007) semantic association questionnaire to graphically represent perceived semantic relationships among the verbs. The data were modeled and visualized using co-occurrence graphs built with Java and Python. The analysis reveals a semantic network comprising various lexical-semantic relations, including synonymy (7.27%), converse antonymy (31.32%), polysemy (2.21%), entailment (24.67%), hyponymy (22.46%), meronymy (2.21%), and collocation (34.49%). Among these, collocational patterns, antonymic contrasts, and entailment relations were most frequent. These results provide insights into the mental lexicon of L2 Persian learners, highlighting conceptual schemas and relational patterns. The study offers pedagogical implications for creating cognitively aligned teaching materials, addressing lexical acquisition gaps, and promoting cluster-based approaches in Persian as a Foreign Language curriculum development.