EAGER: Construction of Inter-Igbo

伊博语是一种有2000万人使用的语言，主要分布在尼日利亚南部。这个急切的项目利用了伊博语口语语料库，语料库将涵盖该语言的所有方言。语料库将用于探索创建统计机器学习(ML)程序以学习由同源词集(伊博语单词在不同位置发音不同但具有相同含义)组成的“间伊博语”，从而使语料库能够被视为一种单一语言，即使在伊博语故乡的两端，方言相互难以理解。我们工作的另一个方面是扩展现有语料库，以填补方言覆盖面的空白，目前有来自没有地理上近邻的地点的录音。之所以需要这样做，是因为一种方言的邻居越接近，他们就越相似，程序就越容易定位系统上不同的单词。这一探索性项目的实现目标是计算语言学的相当大的兴趣。与语言随时间变化相反，关于语言随时间变化的计算工作很少，因此为语言变化的后一个方面找到合适的ML模型是一个相当大的挑战。

Igbo is a language spoken by twenty million people, mostly in southern Nigeria. This EAGER project makes use of a corpus of spoken Igbo, which will cover all of the dialects of the language. The corpus is to be used for explorations in which statistical machine learning (ML) programs are created to learn an "inter-Igbo'' consisting cognate sets (Igbo words pronounced differently in different locations but having the same meaning) that enable the corpus to be treated as if it were spoken as a single language, even though the dialects are, at extreme ends of the Igbo homeland, mutually unintelligible. Another aspect of our work is the extension of the existing corpus to fill in gaps in dialect coverage where there currently are recordings from locations that have no near geographical neighbors. The need for this stems from the fact that the closer a dialect's neighbors the more similar they are, and the easier for programs to locate words which differ systematically.Achieving goals of this exploratory project is of considerable interest for computational linguistics. As opposed to language change over time, there is little computational work on language change over geography, and finding the appropriate ML models for the latter aspect of language variation is a considerable challenge.