Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants

TL;DR

This paper explores how word embeddings can track the evolution of semantic relations over time, using incremental updates and transformations to predict armed conflict participants from geographical data.

Contribution

It introduces a method combining incremental embedding updates and learned transformations to analyze temporal semantic relations in word embeddings.

Findings

Method outperforms baselines in predicting armed conflict groups

Incremental updates effectively capture semantic changes over time

Approach demonstrates feasibility for temporal semantic analysis

Abstract

This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words. The set-up is similar to the well-known analogies task, but expanded with a time dimension. To this end, we apply incremental updating of the models with new training texts, including incremental vocabulary expansion, coupled with learned transformation matrices that let us map between members of the relation. The proposed approach is evaluated on the task of predicting insurgent armed groups based on geographical locations. The gold standard data for the time span 1994--2010 is extracted from the UCDP Armed Conflicts dataset. The results show that the method is feasible and outperforms the baselines, but also that important work still remains to be done.