Classifying New Words for Robust Parsing

Alexander Franz

Classifying New Words for Robust Parsing

Alexander Franz

Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:226-232, 1995.

Abstract

Robust natural language parsing systems must be able to handle words that are not in their lexicons. This paper describes a statistical classifier that determines the most likely parts of speech of new words. The classifier uses a loglinear model to obtain smoothed conditional probabilities that take into account the interactions between different features. We show accuracy results for this model, and compare it to some simpler methods.

Cite this Paper

BibTeX


@InProceedings{pmlr-vR0-franz95a,
  title = 	 {Classifying New Words for Robust Parsing},
  author =       {Franz, Alexander},
  booktitle = 	 {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {226--232},
  year = 	 {1995},
  editor = 	 {Fisher, Doug and Lenz, Hans-Joachim},
  volume = 	 {R0},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {04--07 Jan},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/r0/franz95a/franz95a.pdf},
  url = 	 {https://proceedings.mlr.press/r0/franz95a.html},
  abstract = 	 {Robust natural language parsing systems must be able to handle words that are not in their lexicons. This paper describes a statistical classifier that determines the most likely parts of speech of new words. The classifier uses a loglinear model to obtain smoothed conditional probabilities that take into account the interactions between different features. We show accuracy results for this model, and compare it to some simpler methods.},
  note =         {Reissued by PMLR on 01 May 2022.}
}

Endnote

%0 Conference Paper
%T Classifying New Words for Robust Parsing
%A Alexander Franz
%B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics
%C Proceedings of Machine Learning Research
%D 1995
%E Doug Fisher
%E Hans-Joachim Lenz	
%F pmlr-vR0-franz95a
%I PMLR
%P 226--232
%U https://proceedings.mlr.press/r0/franz95a.html
%V R0
%X Robust natural language parsing systems must be able to handle words that are not in their lexicons. This paper describes a statistical classifier that determines the most likely parts of speech of new words. The classifier uses a loglinear model to obtain smoothed conditional probabilities that take into account the interactions between different features. We show accuracy results for this model, and compare it to some simpler methods.
%Z Reissued by PMLR on 01 May 2022.

APA


Franz, A.. (1995). Classifying New Words for Robust Parsing. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:226-232 Available from https://proceedings.mlr.press/r0/franz95a.html. Reissued by PMLR on 01 May 2022.

Related Material

Download PDF