Paper: Centrality Measures In Text Mining: Prediction Of Noun Phrases That Appear In Abstracts

ACL ID P05-2018
Title Centrality Measures In Text Mining: Prediction Of Noun Phrases That Appear In Abstracts
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors
  • Zhuli Xie (University of Illinois at Chicago, Chicago IL)

Zhuli Xie Department of Computer Science University of Illinois at Chicago Chicago, IL 60607, U. S. A zxie@cs.uic.edu Abstract In this paper, we study different centrality measures being used in predicting noun phrases appearing in the abstracts of sci- entific articles. Our experimental results show that centrality measures improve the accuracy of the prediction in terms of both precision and recall. We also found that the method of constructing Noun Phrase Network significantly influences the accuracy when using the centrality heuristics itself, but is negligible when it is used together with other text features in decision trees.