Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features

@article{Wieling2011BipartiteSG,
  title={Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features},
  author={Martijn Wieling and John Nerbonne},
  journal={Computer Speech & Language},
  year={2011},
  volume={25},
  pages={700-715}
}
In this study we use bipartite spectral graph partitioning to simultaneously cluster varieties and identify their most distinctive linguistic features in Dutch dialect data. While clustering geographical varieties with respect to their features, e.g. pronunciation, is not new, the simultaneous identification of the features which give rise to the geographical clustering presents novel opportunities in dialectometry. Earlier methods aggregated sound differences and clustered on the basis of… CONTINUE READING
BETA

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 23 CITATIONS

References

Publications referenced by this paper.
SHOWING 1-10 OF 31 REFERENCES

Phonology & Morphology of Dutch & Frisian Dialects in 1.1 million transcriptions, Goeman-Taeldeman-Van Reenen project 1980-1995

  • B. van den Berg
  • Meertens Instituut Electronic Publications in…
  • 2003
Highly Influential
10 Excerpts

Word association norms

  • K. W. Church, P. Hanks
  • mutual information, and lexicography, Comput…
  • 1990
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…