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Extracting Fine-Grained Entities Based on Coordinate Graph

  • Qing Yang
  • Peng Jiang
  • Chunxia Zhang
  • Zhendong Niu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)

Abstract

Most previous entity extraction studies focus on a small set of coarse-grained classes, such as person etc. However, the distribution of entities within query logs of search engine indicates that users are more interested in a wider range of fine-grained entities, such as GRAMMY winner and Ivy League member etc. In this paper, we present a semi-supervised method to extract fine-grained entities from an open-domain corpus. We build a graph based on entities in coordinate lists, which are html nodes with the same tag path of the DOM trees. Then class labels are propagated over the graph from known entities to unknowns. Experiments on a large corpus from ClueWeb09a dataset show that our proposed approach achieves the promising results.

Keywords

Fine-Grained Entity Extraction Coordinate Graph Label Propagation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qing Yang
    • 1
  • Peng Jiang
    • 2
  • Chunxia Zhang
    • 3
  • Zhendong Niu
    • 1
  1. 1.School of Computer ScienceBeijing Institute of TechnologyChina
  2. 2.HP LabsChina
  3. 3.School of SoftwareBeijing Institute of TechnologyChina

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