Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge BasesIn this paper, we use the terms ”concept” and ”entity” interchangeably.
Table 2: Results of the concept categorization task, given as percent accuracy (bold indicates best obtained accuracy). Our CME model with bootstrapping gives the best results outperforming all other models and baselines.
['Dataset/Instances', 'Battig', 'DOTA-single', 'DOTA-mult', 'DOTA-all']
Accuracy is calculated by dividing the number of correctly classified concepts by the total number of concepts in the given dataset. Scores of all non-baseline methods are obtained from Li et al It even achieves 100% accuracy on the Battig dataset. With single word concepts, CME achieves the best performance on Battig and competitive performance to WEb on DOTA-single. When it comes to multiword concepts, our CME model comes second after HCE. In general, baselines which depend only on pure concept-concept contexts (Wiki-ccb and Probase-ccb) perform worse than the word-word contexts baseline (WEb). This indicates the significance of the full concept contextual information obtained when including both other nearby words and other nearby concepts while learning target concept representation.