Paper: Relation Extraction with Matrix Factorization and Universal Schemas

ACL ID N13-1008
Title Relation Extraction with Matrix Factorization and Universal Schemas
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Traditional relation extraction predicts rela- tions 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 struc- tured sources of the same schema. The need for existing datasets can be avoided by us- ing a universal schema: the union of all in- volved schemas (surface form predicates as in OpenIE, and relations in the schemas of pre- existing databases). This schema has an al- most unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for en- tity tuples and relation...