Incorporating Both Distributional and Relational Semantics in Word Representations
Table 1: Knowledge Base Completion results: counts by WordNet relation type and test classification accuracies for NTN, TransE, and joint objectives.
['Relationship Type', 'Number of Relationships Train', 'Number of Relationships Dev', 'Number of Relationships Test', 'Classification Accuracies by Model on Test Set NTN', 'Classification Accuracies by Model on Test Set NTN + NLM', 'Classification Accuracies by Model on Test Set TransE', 'Classification Accuracies by Model on Test Set TransE + NLM']
When either NTN or TransE is used as the relational objective, WordNet relationship tuples (s1,R,s2) For comparison with the existing work of Socher et al. The entire training set is presented to the network in randomized order, one instance at a time, during each iteration of training. Models trained using a joint objective (NLM with either TransE or NTN) Similarly, TransE+NLM (83.10%) outperforms the TransE baseline (82.87%). We conclude that ADMM can give small albeit noticeable improvement to TransE and NTN. We note that TransE+NLM performs at least as well as the TransE single objective across all categories except TypeOf and MemberHolonym.