论文标题
马尔可夫链蒙特 - 卡洛系统发育推断在计算历史语言学中
Markov Chain Monte-Carlo Phylogenetic Inference Construction in Computational Historical Linguistics
论文作者
论文摘要
结果，如今，世界上越来越多的语言正在研究中，历史语言学研究的传统方式正在面临一些挑战。例如，语言之间的语言比较研究需要手动注释，随着世界各地越来越多的语言数据量，这变得越来越不可能。尽管它几乎无法取代语言学家的工作，但已经考虑了自动计算方法，它可以帮助人们减少工作量。历史语言学中最重要的工作之一是从不同语言的单词比较，并为它们找到同源单词，这意味着人们试图弄清两种语言是否相互关联。在本文中，我将使用计算方法聚集语言，并使用马尔可夫链蒙特卡洛（MCMC）方法来建立基于簇的语言类型关系树。
More and more languages in the world are under study nowadays, as a result, the traditional way of historical linguistics study is facing some challenges. For example, the linguistic comparative research among languages needs manual annotation, which becomes more and more impossible with the increasing amount of language data coming out all around the world. Although it could hardly replace linguists work, the automatic computational methods have been taken into consideration and it can help people reduce their workload. One of the most important work in historical linguistics is word comparison from different languages and find the cognate words for them, which means people try to figure out if the two languages are related to each other or not. In this paper, I am going to use computational method to cluster the languages and use Markov Chain Monte Carlo (MCMC) method to build the language typology relationship tree based on the clusters.
