Mahesh Akavarapu

KD-108, CSE

IIT Kanpur, Kanpur

U.P., India 208016

maheshak@iitk.ac.in

I am a final year PhD Student in Computer Science at Indian Institute of Technology Kanpur working in Computational Linguitics under the supervision of Prof. Arnab Bhattacharya.

During my PhD, I have developed algorithms for several Computational Historical Linguistic problems namely phonological reconstruction, cognate detection and statistical testing of genetic relationship among languages employing language models and other techiniques largely inspired by Evolutionary Biology. I am also currently working on Low Resource Natural Language Understanding in case of Sanskrit using LLMs. (Research Statement)

I am currently applying for Postdoctoral positions, with the aim of starting in the fall of this year.

selected publications

NAACL

A Likelihood Ratio Test of Genetic Relationship among Languages

V.S.D.S.Mahesh Akavarapu, and Arnab Bhattacharya

In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , Jun 2024

Abs HTML PDF Code

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan.