  Based on the Aristotelian concept of potentiality vs. actuality allowing for
the study of energy and dynamics in language, we propose a field approach to
lexical analysis. Falling back on the distributional hypothesis to
statistically model word meaning, we used evolving fields as a metaphor to
express time-dependent changes in a vector space model by a combination of
random indexing and evolving self-organizing maps (ESOM). To monitor semantic
drifts within the observation period, an experiment was carried out on the term
space of a collection of 12.8 million Amazon book reviews. For evaluation, the
semantic consistency of ESOM term clusters was compared with their respective
neighbourhoods in WordNet, and contrasted with distances among term vectors by
random indexing. We found that at 0.05 level of significance, the terms in the
clusters showed a high level of semantic consistency. Tracking the drift of
distributional patterns in the term space across time periods, we found that
consistency decreased, but not at a statistically significant level. Our method
is highly scalable, with interpretations in philosophy.
