Novel association measures using web search with double checking

  • Authors:

  • Hsin-Hsi Chen;Ming-Shun Lin;Yu-Chuan Wei

  • Affiliations:

  • National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan

  • Venue:
  • ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
  • Year:
  • 2006

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Abstract

A web search with double checking model is proposed to explore the web as a live corpus. Five association measures including variants of Dice, Overlap Ratio, Jaccard, and Cosine, as well as Co-Occurrence Double Check (CODC), are presented. In the experiments on Rubenstein-Goodenough's benchmark data set, the CODC measure achieves correlation coefficient 0.8492, which competes with the performance (0.8914) of the model using WordNet. The experiments on link detection of named entities using the strategies of direct association, association matrix and scalar association matrix verify that the double-check frequencies are reliable. Further study on named entity clustering shows that the five measures are quite useful. In particular, CODC measure is very stable on word-word and name-name experiments. The application of CODC measure to expand community chains for personal name disambiguation achieves 9.65% and 14.22% increase compared to the system without community expansion. All the experiments illustrate that the novel model of web search with double checking is feasible for mining associations from the web.