Modeling the relationship among linguistic typological features with Hierarchical dirichlet process

Chu Cheng Lin, Yu Chun Wang, Richard Tzong Han Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We propose that topic models can be used to represent the relationship among linguistic typological features. Typological features are typically analyzed in terms of universal implications. We argue that topic models can better capture some phenomena, such as universal tendencies, which are hard to be explained by implications. We conduct experiments to evaluate the predictive accuracy of our Hierarchical Dirichlet Process (HDP) model on the WALS dataset. We discover some interesting findings. Topics regarding word order types are recognized. We also find a topic that regards areal tendency.

Original languageEnglish
Title of host publicationPACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation
Pages741-747
Number of pages7
StatePublished - 2009
Event23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23 - Hong Kong, China
Duration: 3 Dec 20095 Dec 2009

Publication series

NamePACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation
Volume2

Conference

Conference23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23
Country/TerritoryChina
CityHong Kong
Period3/12/095/12/09

Keywords

  • Hierarchical dirichlet process
  • Topic model
  • Typological feature

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