@InProceedings{murawaki:2017:I17-1,
  author    = {Murawaki, Yugo},
  title     = {Diachrony-aware Induction of Binary Latent Representations from Typological Features},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {451--461},
  abstract  = {Although features of linguistic typology are a promising alternative to lexical
	evidence for tracing evolutionary history of languages, a large number of
	missing values in the dataset pose serious difficulties for statistical
	modeling.
	In this paper, we combine two existing approaches to the problem: (1) the
	synchronic approach that focuses on interdependencies between features and (2)
	the diachronic approach that exploits phylogenetically- and/or
	spatially-related languages.
	Specifically, we propose a Bayesian model that (1) represents each language as
	a sequence of binary latent parameters encoding inter-feature dependencies and
	(2) relates a language's parameters to those of its phylogenetic and spatial
	neighbors.
	Experiments show that the proposed model recovers missing values more
	accurately than others and that induced representations retain phylogenetic and
	spatial signals observed for surface features.},
  url       = {http://www.aclweb.org/anthology/I17-1046}
}