Capturing Phylogenetic Algorithms for Linguistics

The past decade has seen an explosion in the availability of large datasets of both structural and lexical linguistic data (e.g., Greenhill et al. 2008, Moran et al 2014, Dryer & Haspelmath 2013, Wichmann et al. 2013). These data can be profitably exploited computationally to explore linguistic history and linguistic universals. Work in Bioinformatics has addressed parallel sets of questions for data on living species and a large array of computational modeling and techniques have been developed towards this goal (Felsenstein 2004). The workshop aims to bring together linguists, biologists and mathematicians to make progress on the following topics: * Modeling of Linguistic Data: What are theoretically or empirically the characteristics of linguistic data? Are there bounded rates of change? What are the limits and expectancy of borrowing? Are there universal tendencies manifest in linguistic data? (cf. Sankoff 1973, Field 1998, Blust 2000, Dediu & Cysouw 2013). * Algorithms for Inferences on Linguistic Data: A rich set of questions whose input is linguistic data and output is a model with specifics inferred from the input data (cf. Arapov & Xerts 1974, Embleton 1986), e.g., a subclassification tree (cf. François 2014), a scenario of linguistic diffusion (cf. Chang & Michael 2014), a historical sequence of events (cf. Bouckaert et al 2012), a model of a cognitive domain (cf. Dunn et al 2011a), a geographical division (cf. Wichmann et al 2010, Muysken et al 2015, Prokic & Cysouw 2013) and so on. Both descriptions of novel algorithms to solve such problems and improved algorithms (including approximation algorithms) to solve extant problems are solicited. * Case studies of Specific Families/Regions: Case studies of computational work addressing a specific language family, region or linguistic subdomain with a comparison to the state-of-the-art of non-computational work addressing the same question(s) (cf.  Dunn et al 2011a, Cysouw & Forker 2009, Huffman 1998). * Synergies between Linguistic and Non-Linguistic Data: Computational models exploiting linguistic and non-linguistic data (e.g., archaeological, genetic, ethnographic) tied to the same people/domain to reveal interpretable symmetries and/or discrepancies (cf. Walker et al 2012, Jordan & Huber 2013).