Raising the bar: Stacked conservative error correction beyond boosting

Dekai Wu, Grace Ngai, Marine Carpuat

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

We introduce a conservative error correcting model, Stacked TBL, that is designed to improve the performance of even high-performing models like boosting, with little risk of accidentally degrading performance. Stacked TBL is particularly well suited for corpus-based natural language applications involving high-dimensional feature spaces, since it leverages the characteristics of the TBL paradigm that we appropriate. We consider here the task of automatically annonating named entities in text corpora. The task does pose a number of challenges for TBL, to which there are some simple yet effective solutions. We discuss the empirical behavior of Stacked T BL, and consider evidence that despite its simplicity, more complex and time-consuming variants are not generally required.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Language Resources and Evaluation, LREC 2004
PublisherEuropean Language Resources Association (ELRA)
Pages21-24
Number of pages4
ISBN (Electronic)2951740816, 9782951740815
Publication statusPublished - 1 Jan 2004
Event4th International Conference on Language Resources and Evaluation, LREC 2004 - Lisbon, Portugal
Duration: 26 May 200428 May 2004

Conference

Conference4th International Conference on Language Resources and Evaluation, LREC 2004
CountryPortugal
CityLisbon
Period26/05/0428/05/04

ASJC Scopus subject areas

  • Library and Information Sciences
  • Education
  • Language and Linguistics
  • Linguistics and Language

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