Paper: Typed Tensor Decomposition of Knowledge Bases for Relation Extraction

ACL ID D14-1165
Title Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

While relation extraction has traditionally been viewed as a task relying solely on textual data, recent work has shown that by taking as input existing facts in the form of entity-relation triples from both knowl- edge bases and textual data, the perfor- mance of relation extraction can be im- proved significantly. Following this new paradigm, we propose a tensor decompo- sition approach for knowledge base em- bedding that is highly scalable, and is es- pecially suitable for relation extraction. By leveraging relational domain knowl- edge about entity type information, our learning algorithm is significantly faster than previous approaches and is better able to discover new relations missing from the database. In addition, when ap- plied to a relation extraction task, our ap- proach alone...