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In this paper we described a framework for leveraging large scale knowledge bases to improve relation extraction by training on pairs but using all other Knowledge Bases triples as well

Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction.

empirical methods in natural language processing, (2013): 1366-1371

Cited by: 228|Views93
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Abstract

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 two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empiricall...More

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Introduction
  • Information extraction (IE) aims at generating structured data from free text in order to populate Knowledge Bases (KBs).
  • One is given an incomplete KB composed of a set of triples of the form (h, r , t); h is the left-hand side entity, t the right-hand side entity and r the relationship linking them.
  • This paper focuses on the problem of learning to perform relation extraction (RE) under weak supervision from a KB.
  • RE is sub-task of IE that considers that entities have already been detected by a different process, such as a named-entity recognizer.
  • RE aims at assigning to a relation mention m
Highlights
  • Information extraction (IE) aims at generating structured data from free text in order to populate Knowledge Bases (KBs)
  • One is given an incomplete KB composed of a set of triples of the form (h, r, t); h is the left-hand side entity, t the right-hand side entity and r the relationship linking them
  • This paper focuses on the problem of learning to perform relation extraction (RE) under weak supervision from a KB
  • Figure 1 displays the aggregate precision / recall curves of our approach WSABIEM2R+FB which uses the combination of Sm2r + Skb, as well as WSABIEM2R, which only uses Sm2r, and existing state-of-the-art approaches: HOFFMANN (Hoffmann et al, 2011)2, MIMLRE (Surdeanu et al, 2012)
  • In this paper we described a framework for leveraging large scale knowledge bases to improve relation extraction by training on pairs but using all other KB triples as well
  • We empirically showed that it allows to significantly improve precision on extracted relations
Results
  • The addition of extra knowledge from other Freebase entities in WSABIEM2R+FB provides superior performance to all other methods, by a wide margin, at least between 0 and 0.1 recall.
  • WSABIEM2R+FB for recall > 0.1 degrades rapidly, faster than that of other methods.
  • This is caused by the simplifications of WSABIEM2R that prevent it from reaching high precision when the recall is greater than 0.1.
  • The authors recall that Freebase data is not used to detect relationships i.e. to discriminate between NA and the rest, but only to select the best relationship in case of detection
Conclusion
  • In this paper the authors described a framework for leveraging large scale knowledge bases to improve relation extraction by training on pairs but using all other KB triples as well.
  • The authors' modeling approach is general and should apply to other settings, e.g. for the task of entity linking
Funding
  • This work was carried out in the framework of the Labex MS2T (ANR-11-IDEX-0004-02), and funded by the French National Agency for Research (EVEREST-12-JS02-005-01)
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