Alignment Analysis of Sequential Segmentation of Lexicons to Improve Automatic Cognate Detection

November 20, 2018 · Entered Twilight · 🏛 Annual Meeting of the Association for Computational Linguistics

"Last commit was 7.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, LICENSE, Notebook 1, Shingling.ipynb, Notebook 2, Graphical Error Modelling.ipynb, Notebook 3, Similarity Functions.ipynb, Notebook 4, Error Modelling Function.ipynb, Notebook 5, Final Scoring Function.ipynb, README.md, data, demo.py, src, utils

Authors Pranav A arXiv ID 1811.08129 Category cs.IR: Information Retrieval Cross-listed cs.CL Citations 2 Venue Annual Meeting of the Association for Computational Linguistics Repository https://github.com/pranav-ust/cognates ⭐ 5 Last Checked 2 months ago

Abstract

Ranking functions in information retrieval are often used in search engines to recommend the relevant answers to the query. This paper makes use of this notion of information retrieval and applies onto the problem domain of cognate detection. The main contributions of this paper are: (1) positional segmentation, which incorporates the sequential notion; (2) graphical error modelling, which deduces the transformations. The current research work focuses on classification problem; which is distinguishing whether a pair of words are cognates. This paper focuses on a harder problem, whether we could predict a possible cognate from the given input. Our study shows that when language modelling smoothing methods are applied as the retrieval functions and used in conjunction with positional segmentation and error modelling gives better results than competing baselines, in both classification and prediction of cognates. Source code is at: https://github.com/pranav-ust/cognates