Evolution of semantic networks in biomedical texts

TL;DR

This study investigates the hierarchical structure of semantic networks in scientific texts, revealing how their organization evolves during manuscript revisions and varies with collaboration, reflecting a balance between complexity and efficiency.

Contribution

It demonstrates that semantic networks in scientific articles exhibit Rentian scaling and that the scaling exponent changes systematically during revisions and across collaborative contexts.

Findings

Semantic networks display clear Rentian scaling.

The Rent exponent varies across manuscript revisions.

Final Rent exponent negatively correlates with number of authors.

Abstract

Language is hierarchically organized: words are built into phrases, sentences, and paragraphs to represent complex ideas. Here we ask whether the organization of language in written text displays the fractal hierarchical architecture common in systems optimized for efficient information transmission. We test the hypothesis that the expositional structure of scientific research articles displays Rentian scaling, and that the exponent of the scaling law changes as the article's information transmission capacity changes. Using 32 scientific manuscripts - each containing between three and 26 iterations of revision - we construct semantic networks in which nodes represented unique words in each manuscript, and edges connect nodes if two words appeared within the same 5-word window. We show that these semantic networks display clear Rentian scaling, and that the Rent exponent varies over the publication life cycle, from the first draft to the final revision. Furthermore, we observe that manuscripts fell into three clusters in terms of how the scaling exponents changed across drafts: exponents rising over time, falling over time, and remaining relatively stable over time. This change in exponent reflects the evolution in semantic network structure over the manuscript revision process, highlighting a balance between network complexity, which increases the exponent, and network efficiency, which decreases the exponent. Lastly, the final value of the Rent exponent is negatively correlated with the number of authors. Taken together, our results suggest that semantic networks reflecting the structure of exposition in scientific research articles display striking hierarchical architecture that arbitrates tradeoffs between competing constraints on network organization, and that this arbitration is navigated differently depending on the social environment characteristic of the collaboration.