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Generating Pseudo-ground Truth for Predicting New Concepts in Social Streams

  • David Graus
  • Manos Tsagkias
  • Lars Buitinck
  • Maarten de Rijke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

The manual curation of knowledge bases is a bottleneck in fast paced domains where new concepts constantly emerge. Identification of nascent concepts is important for improving early entity linking, content interpretation, and recommendation of new content in real-time applications. We present an unsupervised method for generating pseudo-ground truth for training a named entity recognizer to specifically identify entities that will become concepts in a knowledge base in the setting of social streams. We show that our method is able to deal with missing labels, justifying the use of pseudo-ground truth generation in this task. Finally, we show how our method significantly outperforms a lexical-matching baseline, by leveraging strategies for sampling pseudo-ground truth based on entity confidence scores and textual quality of input documents.

Keywords

Knowledge Base Ground Truth Entity Recognition Textual Quality Anchor Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Graus
    • 1
  • Manos Tsagkias
    • 1
  • Lars Buitinck
    • 1
  • Maarten de Rijke
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands

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