Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Table 1: SynGCN Intrinsic Evaluation: Performance on word similarity (Spearman correlation), concept categorization (cluster purity), and word analogy (Spearman correlation). Overall, SynGCN outperforms other existing approaches in 9 out of 10 settings. Please refer to Section 9.1 for more details.
['Method', 'Word Similarity WS353S', 'Word Similarity WS353R', 'Word Similarity SimLex999', 'Word Similarity RW', 'Concept Categorization AP', 'Concept Categorization Battig', 'Concept Categorization BLESS', 'Concept Categorization ESSLI', 'Word Analogy SemEval2012', 'Word Analogy MSR']
We report Spearman correlation for word similarity and analogy tasks and cluster purity for concept categorization task. Overall, we find that SynGCN, our proposed method, outperforms all the existing word embedding approaches in 9 out of 10 settings. On average, we obtain around 1.5%, 5.7% and 7.5% absolute increase in performance on word similarity, concept categorization and analogy tasks compared to the best performing baseline. The results demonstrate that the learned embeddings from SynGCN more effectively capture semantic and syntactic properties of words.