Data modelling in corpus linguistics: how low may we go?

Area of Science:

• Computational Linguistics
• Neuroscience
• Literary Studies

Background:

• Corpus linguistics enables analysis of large text volumes.
• Studies have explored links between authors' linguistic decline and diseases like Alzheimer's Disease (AD).
• Numerical data alone is insufficient for interpreting literary analysis; a robust methodology is needed.

Purpose of the Study:

• To develop a robust, unifying computational approach for analyzing temporal dynamics of authors' linguistic richness.
• To model individual authors' linguistic evolution throughout their careers.
• To investigate potential correlations between linguistic changes and pathological conditions.

Main Methods:

• A computerised, individualized approach grounded in Information Theory.
• Analysis of lexical richness dynamics over an author's lifetime.
• Application to six prolific authors with extensive writing careers.

Main Results:

• Identified diverse patterns of lexical richness evolution among the studied authors.
• Observed distinct temporal dynamics in linguistic richness.
• These patterns showed plausible correlations with authors' health conditions, including suspected or diagnosed Alzheimer's Disease.

Conclusions:

• The proposed methodology provides a robust model for tracking linguistic richness changes.
• Linguistic analysis can reveal individual patterns of decline.
• The findings suggest a potential link between lexical richness dynamics and neurodegenerative conditions.