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: this https URL

本篇论文利用信息检索中的排名函数，应用于同源检测，针对同源检测的难点，使用语言模型平滑方法和位置分割与错误建模技术，相比其他基线方法在分类和预测中表现更佳。