EAGER: Phylo: Phylogenetic Reconstruction of Textual Histories

EAGER：Phylo：文本历史的系统发育重建

• 批准号: 1011778
• 负责人: David Chiang
• 金额: $ 7.5万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1011778&HistoricalAwards=false
• 关键词: EAGER; Phylo; Phylogenetic; Reconstruction; Textual

This project, supported by an EArly-concept Grant for Exploratory Research (EAGER), is developing computational models of how manuscripts of premodern texts changed over time due to copying with errors, intentional editing, and translation into different languages. The purpose of these models is to reconstruct the original texts and to better understand the forces that shaped them. We are building on work applying ideas from computational evolutionary biology to the task, but the main focus of the project is to explore whether cutting-edge ideas from computational linguistics and natural language processing are better suited for modeling the evolution of natural-language texts. In particular we are exploring the use of techniques from nonprojective dependency parsing to model the tree of relationships among manuscripts and statistical machine translation to model the relationship between pairs of manuscripts.The tools that result from the project will be made publicly available in order to foster cross-disciplinary research. These tools will enable scholars of ancient and medieval literature to use our models to analyze collections of manuscripts that may not have been possible to analyze by hand before. The techniques explored will shed light on computationally hard learning and search problems such as those that frequently arise in natural language processing.

该项目由EARLY概念探索性研究资助（EAGER）支持，正在开发计算模型，以了解前现代文本的手稿如何随着时间的推移而变化，这是由于复制错误，故意编辑和翻译成不同的语言。这些模型的目的是重建原始文本，并更好地理解塑造它们的力量。我们正在将计算进化生物学的思想应用到这项任务中，但该项目的主要重点是探索计算语言学和自然语言处理的前沿思想是否更适合模拟自然语言文本的进化。特别是，我们正在探索使用非投射依赖分析技术来模拟手稿之间的关系树，以及统计机器翻译来模拟手稿之间的关系。该项目的工具将公开提供，以促进跨学科研究。这些工具将使古代和中世纪文学的学者能够使用我们的模型来分析以前可能无法手工分析的手稿集。探索的技术将揭示计算困难的学习和搜索问题，如那些经常出现在自然语言处理。