Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
Table 2: Impact of each feature representation. “− word” indicates the removal of surface and lemma features. “− cases” and “− path” indicate the removal of the case markers of other dependents and binary path features, respectively. The task Zero is equivalent to the cases where the dependency distance ≥2.
['Model', 'Binary feats.', 'All  [ITALIC] F1 ( [ITALIC] σ)', 'All Prec.', 'All Rec.', '[ITALIC] F1 in different dependency distance  [ITALIC] Dep', '[ITALIC] F1 in different dependency distance  [ITALIC] Zero', '[ITALIC] F1 in different dependency distance 2', '[ITALIC] F1 in different dependency distance 3', '[ITALIC] F1 in different dependency distance 4', '[ITALIC] F1 in different dependency distance ≥ 5']
Compared with the model using all the binary features, the model without this feature drops by 0.3 point in F1 for directly dependent arguments (Dep), and 1.6 points for indirectly dependent arguments (Zero). The result shows that this information significantly improves the prediction in both Dep and Zero cases, especially on Zero argument detection.