Intrinsic Subspace Evaluation of Word Embedding Representations
Table 1: Entity typing results using embeddings learned with different models.
['[EMPTY]', 'all entities MLP', 'all entities 1NN', 'head entities MLP', 'head entities 1NN', 'tail entities MLP', 'tail entities 1NN']
The MLP models consistently outperform 1NN on all and tail entities. This supports our hypothesis that only part of the information about types that is present in the vectors can be determined by similarity-based methods that use the overall direction of vectors, i.e., full-space similarity.