Corpus ID: 2687019

Relation Extraction with Matrix Factorization and Universal Schemas

@inproceedings{Riedel2013RelationEW,
  title={Relation Extraction with Matrix Factorization and Universal Schemas},
  author={S. Riedel and Limin Yao and A. McCallum and Benjamin M Marlin},
  booktitle={HLT-NAACL},
  year={2013}
}

S. Riedel, Limin Yao, +1 author Benjamin M Marlin

Published in HLT-NAACL 2013

Computer Science

© 2013 Association for Computational Linguistics. Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). [...] Key Method To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our…Expand Abstract

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