2013 RelationExtractionwithMatrixFac

• (Riedel et al., 2013) ⇒ Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. (2013). “Relation Extraction with Matrix Factorization and Universal Schemas.” In: Proceedings of the Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2013).

Subject Headings: Universal Schema, Word Vectorizing Function, Probabilistic KB

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Relation+Extraction+with+Matrix+Factorization+and+Universal+Schemas

2015

• (Toutanova et al., 2015) ⇒ Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. (2015). “Representing Text for Joint Embedding of Text and Knowledge Bases.” In: Empirical Methods in Natural Language Processing (EMNLP-2015).
  • QUOTE: In this paper we build upon the work of Riedel et al. (2013), which jointly learns continuous representations for knowledge base and textual relations. This common representation in the same vector space can serve as a kind of “universal schema” which admits joint inferences among KBs and text. The textual relations represent the relationships between entities expressed in individual sentences (see Figure 1 for an example). Riedel et al. (2013) represented each textual mention of an entity pair by the lexicalized dependency path between the two entities (see Figure 2). Each such path is treated as a separate relation in a combined knowledge graph including both KB and textual relations. Following prior work in latent feature models for knowledge base completion, every textual relation receives its own continuous representation, learned from the pattern of its co-occurrences in the knowledge graph.

Quotes

Abstract

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). 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 preexisting 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.

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... We represent the probabilistic knowledge base as a matrix with entity-entity pairs in the rows and relations in the columns

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 RelationExtractionwithMatrixFac	Limin Yao Sebastian Riedel Benjamin M. Marlin Andrew McCallum			Relation Extraction with Matrix Factorization and Universal Schemas						2013