A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation
Table 3: Accuracy of adding word embeddings to IMS, with different parameters, on SE-2, SE-3 LS and AW tasks and SE-2007 AW task
['[BOLD] Type', '[BOLD] Size', '[BOLD] Compose', '[BOLD] Scaling', '[BOLD] SE-2  [BOLD] LS', '[BOLD] SE-3  [BOLD] LS', '[BOLD] SE-2  [BOLD] AW', '[BOLD] SE-3  [BOLD] AW', '[BOLD] SE-2007  [BOLD] Fine-', '[BOLD] SE-2007  [BOLD] Coarse-']
As word embeddings with higher dimensions increases the feature space of IMS, this may lead to overfitting on some datasets. We believe, this is why a smaller number of dimensions work better in the LS tasks. We also note that relatively poorer performance in the LS tasks may not necessarily result in poor performance in the AW task.