Natural Language Processing

Riedel, Sebastian, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. "Relation Extraction with Matrix Factorization and Universal Schemas." HLT-NAACL. 2013. 74-84. Abstractuniv-schema_naacl13_paper.pdf

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 struc- tured sources of the same schema. The need for existing datasets can be avoided by using 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 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 approach outperforms state-of-the-art distant supervision.