RI: Small: RUI: Resource-light Morphosyntactic Tagging of Morphologically Complex Languages

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目的主要目标是开发一种既不依赖于目标语言训练数据，也不需要双语词典和平行语料库的标记方法。 跨语言对应关系的研究有助于我们更好地理解：1）什么样的语言属性对形态句法迁移起着关键作用; 2）如何在句法、词汇、形态等不同层次上度量语言相似性; 3）这种方法如何适用于不属于同一家族的对; 4）什么决定了模型的成功，以及5）如何量化它对给定语言对的潜力。通过利用跨语言关系，训练数据的大小和成本显着降低。该项目是理论语言学（特别是类型学和历时语言学）和自然语言处理之间的新交叉。实际的贡献是一个强大的和便携式的系统标记资源贫乏的语言。通过这种新方法，可以快速部署工具来分析突然出现的关键语言。这种方法还可以增强NSF在记录濒危低密度语言方面的举措，因为它正好利用了领域语言学家和母语者可以提供的知识类型。其他好处包括高质量的注释数据，自动派生的多语言词典，新语言的注释方案，新的类型学概括，以及研究生和本科生研究人员在困难和代表性不足的语言上具有丰富的实践经验。

This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5).The main goal of this project is to develop a tagging method which neither relies on target-language training data nor requires bilingual dictionaries and parallel corpora. The main assumption is that a model for the target language can be approximated by language models from one or more related source languages.Exploiting cross-lingual correspondence leads to a better understanding of 1) what linguistic properties are crucial for morphosyntactic transfer; 2) how to measure language similarity at different levels: syntax, lexicon, morphology; 3) how this method applies to pairs that do not belong to the same family; 4) what determines the success of the model, and 5) how to quantify its potential for a given language pair. By exploiting cross-language relationships, the size, and hence cost, of the training data are significantly reduced. This project is a new cross-fertilization between theoretical linguistics (especially typology and diachronic linguistics) and natural language processing. The practical contribution is a robust and portable system for tagging resource-poor languages. With this new approach, it is be possible to rapidly deploy tools to analyze a suddenly critical language. This approach can also enhance NSF's initiatives in documenting endangered low density languages as it leverages exactly the type of knowledge that a field linguist and a native speaker could provide. Additional benefits include high quality annotated data, automatically derived multilingual lexicons, annotation schemes for new languages, new typological generalizations, and graduate and undergraduate researchers with significant experience of highly practical work on difficult and underrepresented languages.