Monitoring Term Drift Based on Semantic Consistency in an Evolving Vector Field
This work addresses the challenge of tracking term drift for natural language processing applications, but it appears incremental as it combines existing methods with a philosophical metaphor.
The paper tackled the problem of monitoring semantic drift in language over time by proposing a field-based approach using random indexing and evolving self-organizing maps on 12.8 million Amazon book reviews, finding high semantic consistency in term clusters at a 0.05 significance level but no statistically significant decrease in consistency over time.
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.