Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Table 3: SemGCN Extrinsic Evaluation: Comparison of different methods for incorporating diverse semantic constraints in SynGCN embeddings on all extrinsic tasks. Refer Section 9.2 for details.
['Method', 'POS', 'SQuAD', 'NER', 'Coref']
For comparing performance on the extrinsic tasks, we first fine-tune SynGCN embeddings using different methods for incorporating semantic information. We observe that while the other methods do not always consistently give improvement over the baseline SynGCN, SemGCN is able to improve upon SynGCN in all settings (better or comparable). Overall, we observe that SynGCN along with SemGCN is the most suitable method for incorporating both syntactic and semantic information.