Title: Fast and unsupervised methods for multilingual cognate clustering,
Abstract: In this paper we explore the use of unsupervised methods for detecting
cognates in multilingual word lists. We use online EM to train sound segment
similarity weights for computing similarity between two words. We tested our
online systems on geographically spread sixteen different language groups of
the world and show that the Online PMI system (Pointwise Mutual Information)
outperforms a HMM based system and two linguistically motivated systems:
LexStat and ALINE. Our results suggest that a PMI system trained in an online
fashion can be used by historical linguists for fast and accurate
identification of cognates in not so well-studied language families.