Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge BasesIn this paper, we use the terms ”concept” and ”entity” interchangeably.
Table 1: Results of analogical reasoning, given as percent accuracy (bold indicates best obtained accuracy). Our CME model gives the best result on semantic analogies and higher overall accuracy than all other models.
['Dataset/Questions', 'Semantic', 'Syntactic', 'All']
As we see, our CME model outperforms all other models by significant percentages on the semantic analogies. The closest performing model (Glove) is ∼10% less accurate. Performance on syntactic analogies is still very competitive to Word2Vecsg_b and GloVe. Overall, our model is ∼ 5% better than the closest performing model.