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.

该奖项是根据2009年的《美国回收与再投资法》（公法111-5）资助的。该项目的主要目标是开发一种标记方法，该方法既不依赖于目标语言培训数据，也不需要双语词典和平行语料库。 主要的假设是，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近似。探索跨语言对应性可以更好地理解1）哪些语言属性对于形态词法转移至关重要； 2）如何在不同层面上衡量语言相似性：语法，词典，形态； 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）如何量化其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大大降低。该项目是理论语言学（尤其是类型学和历时语言学）和自然语言处理之间的新跨批准。实际的贡献是一个可靠的便携式系统，用于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的关键语言。这种方法还可以增强NSF在记录濒临灭绝的低密度语言方面的举措，因为它准确地利用了现场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，自动衍生的多语言词典，新语言的注释方案，新的类型学概括以及研究生和本科研究人员，在困难和代表性不足的语言方面具有丰富的实用性经验。