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Between Century and Poet: Graph-Based Lexical Semantic Change in Persian Poetry

arXiv 2604.06674 ↗First posted 8 Apr 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Meaning in Persian poetry is both historical and relational. Words persist through literary tradition while shifting their force through changing constellations of neighbors, rhetorical frames, and poetic voices. This study examines that process using aligned Word2Vec spaces combined with graph-based neighborhood analysis across centuries and major poets. Rather than modeling semantic change as vector displacement alone, it treats lexical history as the rewiring of local semantic graphs: the gain and loss of neighbors, shifts in bridge roles, and movement across communities. The analysis centers on twenty target words, anchored by five recurrent reference terms: Earth, Night, two wine terms, and Heart. Surrounding them are affective, courtly, elemental, and Sufi concepts such as Love, Sorrow, Dervish, King, Annihilation, and Truth. These words exhibit distinct patterns of change. Night is more time-sensitive, Earth more poet-sensitive, and Heart shows continuity despite graph-role mobility. The two wine terms highlight probe sensitivity: one is broad and semantically diffuse, while the other is narrower and more stable. A lexical audit confirms that the corpus contains historically driven terms, poet-specific usages, and sparsely attested mystical vocabulary requiring caution. Overall, semantic change in Persian poetry is better captured as neighborhood rewiring than as abstract drift. For Digital Humanities, this approach restores local structure to computational analysis and supports interpretations closer to literary practice: persistence, migration, mediation, and selective transformation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 8 Apr 2026.

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References · 7

  1. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 221
  2. 2018A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddingscited by 146
  3. 2018Diachronic word embeddings and semantic shifts: a surveycited by 92
  4. 2017Dynamic Word Embeddingscited by 51
  5. 2019Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shiftcited by 37
  6. 2024Graph-based Clustering for Detecting Semantic Change Across Time and Languagescited by 10
  7. 2026A Dynamic Atlas of Persian Poetic Symbolism: Families, Fields, and the Historical Rewiring of Meaningcited by 1

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