Multilingual Unsupervised Dependency Parsing with Unsupervised POS Tags

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9413)

Abstract

In this paper, we present experiments with unsupervised dependency parser without using any part-of-speech tags learned from manually annotated data. We use only unsupervised word-classes and therefore propose fully unsupervised approach of sentence structure induction from a raw text. We show that the results are not much worse than the results with supervised part-of-speech tags.

Keywords

Grammar induction Unsupervised parsing Word classes Gibbs sampling 
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Notes

Acknowledgments

This research has been supported by the grant no. GPP406/14/06548P of the Grant Agency of the Czech Republic.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Mathematics and Physics, Institute of Formal and Applied LinguisticsCharles University in PraguePragueCzech Republic

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