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

RI：小：RUI：形态复杂语言的轻资源形态句法标记

• 批准号: 0916280
• 负责人: Anna Feldman
• 金额: $ 16.92万
• 依托单位: Montclair State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916280&HistoricalAwards=false
• 关键词: RI; Small; RUI; Resource; light

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.

该奖项由2009年美国复苏与再投资法案（公法111-5）资助。该项目的主要目标是开发一种既不依赖目标语言训练数据，又不需要双语词典和平行语料库的标注方法。 主要假设是目标语言的模型可以通过一种或多种相关源语言的语言模型来近似。利用跨语言对应可以更好地理解：1）哪些语言特性对于形态句法迁移至关重要； 2）如何衡量不同层面的语言相似度：语法、词汇、词法； 3）该方法如何适用于不属于同一家族的配对； 4) 决定模型成功的因素是什么，以及 5) 如何量化其对于给定语言对的潜力。通过利用跨语言关系，训练数据的大小和成本都显着减少。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且可移植的系统，用于标记资源匮乏的语言。通过这种新方法，可以快速部署工具来分析突然变得关键的语言。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，因为它恰好利用了现场语言学家和母语人士可以提供的知识类型。其他好处包括高质量的注释数据、自动生成的多语言词典、新语言的注释方案、新的类型概括，以及在困难和代表性不足的语言方面具有高度实际工作经验的研究生和本科生研究人员。