Abstract
Automatic Cognate Detection (ACD) is a challenging task which has been
utilized to help NLP applications like Machine Translation, Information
Retrieval and Computational Phylogenetics. Unidentified cognate pairs can pose
a challenge to these applications and result in a degradation of performance.
In this paper, we detect cognate word pairs among ten Indian languages with
Hindi and use deep learning methodologies to predict whether a word pair is
cognate or not. We identify IndoWordnet as a potential resource to detect
cognate word pairs based on orthographic similarity-based methods and train
neural network models using the data obtained from it. We identify parallel
corpora as another potential resource and perform the same experiments for
them. We also validate the contribution of Wordnets through further
experimentation and report improved performance of up to 26%. We discuss the
nuances of cognate detection among closely related Indian languages and release
the lists of detected cognates as a dataset. We also observe the behaviour of,
to an extent, unrelated Indian language pairs and release the lists of detected
cognates among them as well.