Learning condensed feature representations from large unsupervised data sets for supervised learning

Jun Suzuki, Hideki Isozaki, Masaaki Nagata

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

10 Citations (Scopus)

Abstract

This paper proposes a novel approach for effectively utilizing unsupervised data in addition to supervised data for supervised learning. We use unsupervised data to generate informative 'condensed feature representations' from the original feature set used in supervised NLP systems. The main contribution of our method is that it can offer dense and low-dimensional feature spaces for NLP tasks while maintaining the state-of-the-art performance provided by the recently developed high-performance semi-supervised learning technique. Our method matches the results of current state-of-the-art systems with very few features, i.e., F-score 90.72 with 344 features for CoNLL-2003 NER data, and UAS 93.55 with 12.5K features for dependency parsing data derived from PTB-III.

Original languageEnglish
Title of host publicationACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
Pages636-641
Number of pages6
Publication statusPublished - 2011 Dec 1
Externally publishedYes
Event49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States
Duration: 2011 Jun 192011 Jun 24

Publication series

NameACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Volume2

Other

Other49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Country/TerritoryUnited States
CityPortland, OR
Period11/6/1911/6/24

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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