Learning Concept Embeddings for Dataless Classification via Efficient Bag of Concepts Densification
Table 5: Accuracy of concept categorization. The CRX model with bootstrapping gives the best results outperforming all other models.
['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 other methods are obtained from \shortciteAli2016joint. While the CCX model performance is much less than CRX. With bootstrapping, the CCX model performance improves on both datasets. CRX with bootstrapping outperforms all other models by significant percentages. These results show that learning concept embeddings from concept mentions is actually different from training the skip-gram model on phrases or multiword expressions. This is clear from the significant performance gains we get from the CRX and CCX models compared to WEMikolov which was trained using skip-gram on phrases. Additionally, the results demonstrate the efficacy of our models which simply learn concept embeddings from concept mentions in free-text corpus compared to the more complex models which require category or relational information such as TransE, CE, and HCE.