One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
Table 3: Average diachronic performance
['[EMPTY]', '[BOLD] Algorithm', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
Table 3 shows the diachronic performance of our system in the setup when the matrix Tn and the threshold rn are applied to the year n + 1. For both Gigaword and NOW datasets (and the corresponding embeddings), using the cosinebased threshold decreases recall and increases precision (differences are statistically significant with t-test, p < 0.05). At the same time, the integral [CONTINUE] metrics of F1 consistently improves (p < 0.01). Thus, the thresholding reduces prediction noise in the one-to-X analogy task without sacrificing too many correct answers. In our particular case, this helps to more precisely detect events of armed conflicts termination (where no insurgents should be predicted for a location), not only their start.