Explore person specific evidence in web person name disambiguation

  • Authors:

  • Liwei Chen;Yansong Feng;Lei Zou;Dongyan Zhao

  • Affiliations:

  • Peking University, Beijing;Peking University, Beijing;Peking University, Beijing;Peking University, Beijing

  • Venue:
  • EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
  • Year:
  • 2012

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Abstract

In this paper, we investigate different usages of feature representations in the web person name disambiguation task which has been suffering from the mismatch of vocabulary and lack of clues in web environments. In literature, the latter receives less attention and remains more challenging. We explore the feature space in this task and argue that collecting person specific evidences from a corpus level can provide a more reasonable and robust estimation for evaluating a feature's importance in a given web page. This can alleviate the lack of clues where discriminative features can be reasonably weighted by taking their corpus level importance into account, not just relying on the current local context. We therefore propose a topic-based model to exploit the person specific global importance and embed it into the person name similarity. The experimental results show that the corpus level topic information provides more stable evidences for discriminative features and our method outperforms the state-of-the-art systems on three WePS datasets.