Reducing noise in labels and features for a real world dataset: Application of NLP corpus annotation methods

Rebecca J. Passonneau, Cynthia Rudin, Axinia Radeva, Zhi An Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper illustrates how a combination of information extraction, machine learning, and NLP corpus annotation practice was applied to a problem of ranking vulnerability of structures (service boxes, manholes) in the Manhattan electrical grid. By adapting NLP corpus annotation methods to the task of knowledge transfer from domain experts, we compensated for the lack of operational definitions of components of the model, such as serious event. The machine learning depended on the ticket classes, but it was not the end goal. Rather, our rule-based document classification determines both the labels of examples and their feature representations. Changes in our classification of events led to improvements in our model, as reflected in the AUC scores for the full ranked list of over 51K structures. The improvements for the very top of the ranked list, which is of most importance for prioritizing work on the electrical grid, affected one in every four or five structures.

Original languageEnglish (US)
Title of host publicationComputational Linguistics and Intelligent Text Processing - 10th International Conference, CICLing 2009, Proceedings
Pages86-97
Number of pages12
DOIs
Publication statusPublished - Jul 21 2009
Event10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009 - Mexico City, Mexico
Duration: Mar 1 2009Mar 7 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009
CountryMexico
CityMexico City
Period3/1/093/7/09

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Passonneau, R. J., Rudin, C., Radeva, A., & Liu, Z. A. (2009). Reducing noise in labels and features for a real world dataset: Application of NLP corpus annotation methods. In Computational Linguistics and Intelligent Text Processing - 10th International Conference, CICLing 2009, Proceedings (pp. 86-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5449 LNCS). https://doi.org/10.1007/978-3-642-00382-0_7