Neural Vector Conceptualization for Word Vector Space Interpretation
Table 1: Precision (P), recall (R), F1 Score (F), and support (S) for all 637 concepts (F1 Score weighted by support) and selected individual concepts. Class membership was determined by an activation threshold of 0.5.
['[EMPTY]', 'P', 'R', 'F', 'S']
The table contains the weighted average scores accomplished for all concepts as well as the scores the model achieved for selected individual concepts, grouped semantically. The weighted classification F1 score is 0.22 which suggests that the task is complex, probably due to the highly imbalanced data set. While we observe a high score for province, our model has difficulties classifying locations, for instance. The same trend can be observed for choreographers and legends. What we see reflected in this table is the sharpness of concept boundaries. Arguably, the definition of a province is sharper than that of location. The same is true for choreographer and legend. We assume that the more precise a concept boundary, the higher the classification performance tends to be. Fig. Fig.