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

随着现代技术基础设施在全世界的普及，以数百种不同语言书写的电子文本的数量在规模和多样性上继续增长。目前，要在如此众多的语言中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，方法是不可能提供一个独立的语言解决这个问题。专注于词性预测，该项目采取了一种新的方法，结合监督和无监督学习的元素，而不假设任何特定的知识的目标语言。 而不是把个别语言作为封闭的系统，语言独立的“共性”的统计估计，从几十种语言的注释语料库存在，这些学习的共性被用来预测的词性类别的未注释的语言。该项目的核心是对语言独立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。这些学习到的模式被纳入表达结构化预测模型使用新的近似学习和推理方法开发的主要研究者的项目。在美国，有数百种语言面临着立即灭绝的危险，还有数千种语言可能在未来几十年内消失。通过促进快速创建独立于语言的语言分析工具，该项目下开发的技术有可能彻底改变濒危语言的文件。从长远来看，这一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部社会效益。

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.