Logician: A unified end-to-end neural approach for open-domain information extraction

Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains 48,248 sentences and the corresponding facts in the SAOKE format labeled by crowdsourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.

Original languageEnglish (US)
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages556-564
Number of pages9
ISBN (Electronic)9781450355810
DOIs
StatePublished - Feb 2 2018
Externally publishedYes
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: Feb 5 2018Feb 9 2018

Publication series

NameWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
Volume2018-Febuary

Other

Other11th ACM International Conference on Web Search and Data Mining, WSDM 2018
CountryUnited States
CityMarina Del Rey
Period2/5/182/9/18

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Deep learning
  • End-to-end learning
  • Knowledge expression
  • Open information extraction
  • Sequence-to-sequence learning

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  • Cite this

    Sun, M., Li, X., Wang, X., Fan, M., Feng, Y., & Li, P. (2018). Logician: A unified end-to-end neural approach for open-domain information extraction. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining (pp. 556-564). (WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining; Vol. 2018-Febuary). Association for Computing Machinery, Inc. https://doi.org/10.1145/3159652.3159712