Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction

This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over methods that rely on text features alone.

• Publication year

  2013

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  2013-07-30

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.18653/v1/d13-1136arXiv 1307.7973
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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