We present a system for identifying cognate sets across dictionaries of related languages. The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences. The similarity scores are used to cluster words from different languages that may originate from a common protoword. When tested on the Algonquian language family, our system detects 63% of cognate sets while maintaining cluster purity of 70%.

We describe our approach and experiments in the context of the SIGMOR-PHON 2016 Shared Task on Morphological Reinflection. The results show that the methods of Nicolai et al. (2015) perform well on typologically diverse languages. We also discuss language-specific heuristics and errors.

We report the results of our experiments in the context of the NEWS 2015 Shared Task on Transliteration. We focus on methods of combining multiple base systems, and leveraging transliterations from multiple languages. We show error reductions over the best base system of up to 10% when using supplemental transliterations, and up to 20% when using system combination. We also discuss the quality of the shared task datasets.