RI: Small: Collaborative Research: Statistical Learning of Language Universals

随着现代技术基础设施遍布世界各地，以数百种不同语言编写的电子文本的数量在规模和多样性上持续增长。目前，要跨多种语言构建有效的信息检索、提取和翻译系统，需要对每种语言进行耗时且昂贵的语言注释。 通用的、完全无监督的方法不太可能为这个问题提供独立于语言的解决方案。该项目专注于词性预测，采用了一种新颖的方法，结合了监督和无监督学习的元素，而不假设目标语言有任何特定知识。 不是将单个语言视为封闭系统，而是从存在注释语料库的数十种语言中统计估计独立于语言的“通用性”，并且这些学习到的通用性用于预测未注释语言的词性类别。该项目的核心是对独立于语言的语料库特征的数据驱动探索，这些特征将跨语言的语言类别与文本的表面统计联系起来。使用该项目的主要研究人员开发的新颖的近似学习和推理方法，将这些学习模式纳入表达性结构化预测模型中。在世界上的口语中，数百种语言面临立即灭绝的风险，而在未来几十年内，还有数千种语言可能会消失。通过促进快速创建独立于语言的语言分析工具，该项目开发的技术有可能彻底改变濒危语言的记录。从长远来看，该研究方向还将通过为数百种低资源语言创建智能文本处理工具，帮助实现全球技术基础设施的全面社会效益。

As modern technology infrastructure spreads throughout the world, the quantity of electronic text, written in hundreds of different languages, continues to grow in size and diversity. Building effective information retrieval, extraction, and translation systems across this vast array of languages currently requires time-consuming and expensive linguistic annotations for each language. Generic, fully unsupervised, methods are unlikely to provide a language independent solution to this problem.Focusing on part-of-speech prediction, this project undertakes a novel approach, combining elements of supervised and unsupervised learning without assuming any specific knowledge of the target language. Instead of treating individual languages as closed systems, language-independent "universals" are statistically estimated from dozens of languages for which annotated corpora exist, and these learned universals are used to predict the part-of-speech categories of unannotated languages. At the heart of the project is a data-driven exploration of language-independent corpus characteristics that relate cross-lingual linguistic categories to surface statistics of text. These learned patterns are incorporated into expressive structured prediction models using novel approximate learning and inference methods developed by the Principal Investigators of the project.Of the world?s spoken languages, hundreds are at risk of immediate extinction and thousands more are likely to disappear over the coming decades. By facilitating the rapid creation of language-independent linguistic analysis tools, the technology developed under this project has the potential to revolutionize the documentation of endangered languages. In the long-term, this research direction will also help realize the full social benefits of the global technology infrastructure by creating intelligent text processing tools for hundreds of low-resource languages.