Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
Table 3: Comparisons to previous work in F1
['Model', 'ALL', 'Dep ALL', 'Dep NOM', 'Dep ACC', 'Dep DAT', 'Zero ALL', 'Zero NOM', 'Zero ACC', 'Zero DAT']
First, among our models, the B model that uses only binary features already outperforms the state-of-the-art global model on NTC 1.5 Ouchi et al. Moreover, the B model outperforms the global model of \newciteOuchi2015 that utilizes the basic feature set hand-crafted by \newciteimamura2009discriminative and \newciteHayashibe2011 and thus contains almost the same binary features as ours. These results show that fine feature combinations learned by deep NN contributes significantly to the performance. The WBP-Shwartz (ens)−{word, path} model, which has the highest performance among our models shows a further 1.8 points improvement in overall F1, which achieves 13% error reduction compared with the state-of-the-art grobal model (81.42% of Ouchi et al.