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Revisiting the Role of Feature Engineering for Compound Type Identification in Sanskrit

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Figure

Table 1. Next to the embedding layer, a drop-out layer with drop-out rate 0.2 is used to avoid
Figure 1: FastText feature augmented with element-wise difference and multiplication of com-pound’s component (Conneau et al., 2017)
Figure 2 presents a few statistics regarding the corpus utilized.
Table 4:Evaluation measures are accuracy (A), macro precision (P), macro recall (R) andmacro F-score (F)
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