Automatic cognate identification with gap-weighted string subsequences.

Abstract

In this paper, we describe the problem of cognate identification in NLP. We introduce the idea of gap-weighted subsequences for discriminating cognates from non-cognates. We also propose a scheme to integrate phonetic features into the feature vectors for cognate identification. We show that subsequence based features perform better than state-ofthe-art classifier for the purpose of cognate identification. The contribution of this paper is the use of subsequence features for cognate identification.