Enterprise AI Analysis
Between Century and Poet: Graph-Based Lexical Semantic Change in Persian Poetry
Meaning in Persian poetry is historical, but it is also profoundly relational. Words endure through repeated literary traditions while changing their force through shifting constellations of neighboring terms, rhetorical frames, and poetic voices. This study examines that process by combining aligned Word2Vec spaces with graph-based neighborhood analysis across century slices and across a selected set of major poets. Rather than treating semantic change as vector displacement alone, it models lexical history as the rewiring of local semantic graphs: the loss and gain of neighbors, altered bridge positions, and movement across communities. A panel of twenty target words structures the analysis, with five recurrent reference words at its center: khaak / Earth, shab / Night, mey / Wine, baadeh / Wine, and del / Heart. Around them stand affective, courtly, elemental, and Sufi terms such as eshgh / Love, gham / Sorrow, darvish / Dervish, shah / King, fanaa / Annihilation, and haqiqat / Truth. Together these words do not participate in a single profile of change. shab / Night is more time-sensitive than poet-sensitive, khaak / Earth is more sharply differentiated by poetic voice, and del / Heart shows strong continuity despite marked graph-role mobility. The wine pair makes probe choice decisive: mey / Wine is broad, unstable, and semantically noisy, whereas baadeh / Wine is cleaner, narrower, and easier to interpret. A lexical audit of affective, elemental, courtly, and Sufi vocabulary confirms that the same corpus contains strongly historical words, strongly poet-shaped words, and sparsely attested mystical terms that demand caution. Across this lexicon, semantic change in Persian poetry is captured more effectively as neighborhood rewiring than as drift in abstract space alone. For Digital Humanities, that shift matters because it restores local structure to computational evidence and allows lexical history to be described in terms closer to literary reading: persistence, migration, mediation, and selective reshaping.
Executive Impact & Key Findings
This research pioneers a graph-based approach to lexical semantic change in Persian poetry, moving beyond traditional vector displacement models. By analyzing the rewiring of semantic neighborhoods, it reveals nuanced patterns of word evolution influenced by historical period and poetic voice. Key findings include distinct change profiles for various lexical domains—some time-sensitive, others poet-sensitive—demonstrating that meaning persists through reassembly rather than stasis. This methodology offers a more interpretable framework for studying lexical history in rich literary traditions.
11
Centuries Analyzed
129,451
Poems Processed
20
Words Tracked
200
Semantic Dimensions
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Beyond Vector Drift: A New Framework
The study introduces a novel graph-based methodology for detecting lexical semantic change, arguing that 'neighborhood rewiring' (changes in word companions, bridge positions, and community membership) provides a richer, more literarily interpretable account of semantic evolution than simple vector displacement or 'drift' in embedding space. This approach restores local structural context to computational evidence.
Diachronic Embeddings & Graph Analysis
Leveraging aligned Word2Vec embeddings across century and poet slices, the research constructs mutual k-nearest-neighbor graphs. It tracks metrics like degree centrality, community assignment, and bridge score volatility, demonstrating how these graph-based measures capture distinct patterns of change (persistence, migration, mediation, reshaping) that are obscured by traditional drift metrics alone.
Time, Poet, and Persian Poetic Semantics
The analysis of a 20-word panel (including 'shab'/'Night', 'khaak'/'Earth', 'mey'/'Wine', 'del'/'Heart') reveals that lexical meaning in Persian poetry is shaped unevenly by historical time and poetic voice. Some words are more time-sensitive, others poet-sensitive, and some exhibit mixed pressures. This provides a nuanced understanding of how shared symbolic repertoires are continually reweighted and reassembled.
Graph-Based Lexical Semantic Change Workflow
Our methodology redefines semantic change detection by focusing on the dynamic rewiring of lexical neighborhoods, offering a nuanced view beyond mere vector displacement.
Corpus Preparation
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Word Embedding Models (Skip-gram)
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Orthogonal Procrustes Alignment
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Mutual k-NN Graph Construction
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Community Detection & Graph Role Measures
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Drift & Turnover Analysis
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Century-Poet Comparison
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Literary Interpretation
Rewiring vs. Drift: Capturing Semantic Change
The study demonstrates that semantic change in Persian poetry is more effectively captured as neighborhood rewiring (loss/gain of neighbors, altered bridge positions) than as simple vector displacement. This shifts the focus from 'where a word moved' to 'how its company changed'.
Neighborhood Rewiring More Effective than Drift| Word | Dominant Pressure | Key Characteristics |
|---|---|---|
| shab / Night | More time-sensitive |
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| khaak / Earth | Mixed (poet-specific) |
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| del / Heart | More time-sensitive |
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| mey / Wine | More time-sensitive |
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| baadeh / Wine | More time-sensitive |
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| eshgh / Love | More poet-sensitive |
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| fanaa / Annihilation | More poet-sensitive |
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| haqiqat / Truth | More poet-sensitive |
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Case Study: The 'Wine' Lexicon (mey vs. baadeh)
The contrast between 'mey' (wine) and 'baadeh' (wine) highlights a crucial methodological insight: 'mey' is semantically expansive, prone to strong drift and turnover across convivial, mystical, satirical, and lyrical registers, making it noisy. 'baadeh', conversely, keeps a narrower, more coherent convivial cluster, making its semantic history more disciplined and interpretable despite its own changes.
Key Learning: Narrower, more focused lexical probes can yield clearer and more interpretable historical semantic narratives, as their neighborhood rewiring remains traceable within defined symbolic fields. This is crucial for nuanced literary analysis.
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