Abstract
Universal schema (USchema) assumes that two sentence patterns that share the same entity pairs are similar to each other. This assumption is widely adopted for solving various types of relation extraction (RE) tasks. Nevertheless, each sentence pattern could contain multiple facets, and not every facet is similar to all the facets of another sentence pattern co-occurring with the same entity pair. To address the violation of the USchema assumption, we propose multi-facet universal schema that uses a neural model to represent each sentence pattern as multiple facet embeddings and encourage one of these facet embeddings to be close to that of another sentence pattern if they co-occur with the same entity pair. In our experiments, we demonstrate that multi-facet embeddings significantly outperform their single-facet embedding counterpart, compositional universal schema (CUSchema) (Verga et al., 2016), in distantly supervised relation extraction tasks. Moreover, we can also use multiple embeddings to detect the entailment relation between two sentence patterns when no manual label is available.
Highlights
Relation extraction (RE) is a crucial step in automatic knowledge base construction (AKBC)
We propose multifacet universal schema, where we assume that two sentence patterns share a similar facet if they cooccur with the same entity pair
The results demonstrate that multiple facet embeddings significantly improve the similarity measurement between the sentence patterns and knowledge base relations
Summary
Relation extraction (RE) is a crucial step in automatic knowledge base construction (AKBC). Universal schema (Riedel et al, 2013) extends this assumption by treating every sentence pattern as a relation, which means we assume that sentence patterns or relations in a knowledge base are similar if they co-occur with the same entity pair. If we squeeze the facets of a sentence pattern into a single embedding, the embedding is more likely to be affected by the irrelevant facets from other patterns co-occurred with the same entity pair (e.g., “$ARG1 moved in with $ARG2” might incorrectly imply the co-worker relation). Another limitation is that single embedding representation can only provide symmetric similarity measurement between two sentence patterns. In a newly collected dataset, we show that multi-facet universal schema significantly outperforms the other unsupervised baselines
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