Tracing armed conflicts with diachronic word embedding models
Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the conflict research field as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting {`}cultural{'} semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the {`}anchor words{'} method which outperforms previous approaches on this set.
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