RI: SMALL: Statistical Linguistic Typology

RI：小：统计语言类型学

• 批准号: 1153487
• 负责人: Hal Daume
• 金额: $ 36.6万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1153487&HistoricalAwards=false
• 关键词: RI; SMALL; Statistical; Linguistic; Typology

This project considers the unification of two view of language: thatfrom natural language processing and that from linguistic typology.Our view is that typological information is both useful for solvingreal-world natural language processing thats and automaticallyderivable from language data. This research first explores how to usetypological knowledge to improve performance on problems such asdependency parsing and machine translation for low density langauges.Intuitively, our statistical models waste time exploring a hypothesisspace that is too big: the space of realistic grammars is much smallerthan the space of all grammars. The second part of this researchconsiders the automatic acquisition and boostrapping of typologicalknowledge from raw text. The outcome of this research is: (a)improved statistical models for hard natural language processingproblems; and (b) a larger library of typological universals that havebeen derived automatically from data. Our outcomes are empiricallyevaluated on the raw language processing tasks and in terms of thequality of the universal implications mined from data, but comparingthem with known repositories of universals. Our results will impact the fields of natural language processing and linguistics. From the research side, this research will find applications in a wider variety of problems than the ones we intend to study; in particular, the use of linguistic universals in natural language processing technologywill fundamentally change the way multilinguality is addressed in thisfield. From a linguistics perspective, the goal of this project is toshed new light on linguistic universals. This should impact not onlythe area of typology, but also the study and preservation ofendangered languages. By automatically identifying typologicalfeatures and implications from data, the process of documentingendangered languages could be made more efficient: leading to asmaller loss of knowledge of these languages.

该项目考虑了两种语言观点的统一：从自然语言处理和语言类型学中。我们的观点是，类型信息既可用于求解核心世界自然语言处理，从而可以从语言数据中自动衍生。 这项研究首先探讨了如何使用USETOGOGIOG知识来改善低密度Langauges的问题的性能，例如Asdepenty解析和机器翻译。直觉上，我们的统计模型浪费了探索太大的假设的统计模型：现实语法的空间是所有语法的较小空间。 该研究界的第二部分是从原始文本中自动获取和逐步启动类型学知识。 这项研究的结果是：（a）改进硬性自然语言处理问题的统计模型； （b）一个较大的类型学普遍性库，它们自动从数据中自动得出。 我们的结果在原始语言处理任务上以及从数据中挖掘出的普遍含义的质量上进行了经验评估，但将其与已知的普遍性存储库进行了比较。 我们的结果将影响自然语言处理和语言学领域。 从研究方面，这项研究将发现与我们打算研究的问题相比，在多种问题中的应用。特别是，语言普遍性在自然语言处理技术中的使用从根本上改变了Thisfield的多语言方式。 从语言学的角度来看，该项目的目标是对语言普遍性的新灯。 这不仅会影响类型学领域，还会影响既定语言的研究和保存。 通过自动识别数据的类型图和含义，可以使记录范围内的语言的过程变得更有效：导致Asmaller对这些语言的了解丧失。