Observed Versus Latent Features for Knowledge Base and Text Inference
3rd Workshop on Continuous Vector Space Models and Their Compositionality |
Published by ACL - Association for Computational Linguistics
In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets – FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed features model is most effective at capturing the information present for entity pairs with textual relations, and a combination of the two combines the strengths of both model types.