Non-linear tagging models with localist and distributed word representations

Sumit Chopra, Srinivas Bangalore

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

Abstract

Distributed representations of words are attractive since they provide a means for measuring word similarity. However, most approaches to learning distributed representations are divorced from the task context. In this paper, we describe a model that learns distributed representations of words in order to optimize task performance. We investigate this model for part-of-speech tagging and supertagging tasks and demonstrate its superior accuracy over localist models, especially for rare words. We also show that adding non-linearity in the model aids in improved accuracy for complex tasks such as supertagging.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2144-2147
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period5/22/115/27/11

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Non-linear tagging models with localist and distributed word representations'. Together they form a unique fingerprint.

Cite this