Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Table 4: SemGCN Intrinsic Evaluation: Evaluation of different methods for incorporating diverse semantic constraints initialized using various pre-trained embeddings (X). M(X, R) denotes the fine-tuned embeddings using method M taking X as initialization embeddings. R denotes the type of semantic relations used as defined in Section 9.2. SemGCN outperforms other methods in 13 our of 15 settings. SemGCN with SynGCN gives the best performance across all tasks (highlighted using ). Please refer Section 9.2 for details.
['Init Embeddings (=X) Datasets', 'Word2vec WS353', 'Word2vec AP', 'Word2vec MSR', 'GloVe WS353', 'GloVe AP', 'GloVe MSR', 'Deps WS353', 'Deps AP', 'Deps MSR', 'EXT WS353', 'EXT AP', 'EXT MSR', 'SynGCN WS353', 'SynGCN AP', 'SynGCN MSR']
Due to space limitations, we report results on one representative dataset per task. We find that in 13 out of 15 settings, SemGCN outperforms other methods.