Lexical Sememe Prediction using Dictionary Definitions by Capturing Local Semantic Correspondence
Table 3: Sememe prediction MAPs for in-vocabulary (IV) and out-of-vocabulary (OOV) words of different models. +TW-WS signifies removing word splitting in the +TW operation.
['Model', 'IV', 'OOV']
From the table, we can observe that our plain SCorP model and its variant SCorP(+TW) dramatically outperform the two baseline methods, especially for the OOV words. This manifests our model’s extraordinary generalization capability to tackle the OOV problem. We also confirm the effectiveness of word splitting workaround by a lesion experiment. After removing word splitting for target words in +TW (denoted by + TW-WS), SCorP(+TW,-WS) model performs considerably worse in sememe prediction for the OOV words, even close to the plain SCorP model making no use of target word embeddings.