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的统一：自然语言处理的语言观和语言类型学的语言观，我们认为类型学信息既有助于解决现实世界的自然语言处理问题，又可以从语言数据中自动导出。 本研究首先探讨了如何使用类型学知识来提高低密度语言的依赖分析和机器翻译等问题的性能。直观地说，我们的统计模型浪费时间探索一个太大的假设空间：现实语法的空间比所有语法的空间小得多。 本研究的第二部分考虑了从原始文本中自动获取和引导类型学知识。 这项研究的成果是：（a）改进了自然语言处理困难问题的统计模型;（B）一个更大的类型学共性库，这些共性是从数据中自动推导出来的。 我们的结果是在原始的语言处理任务和从数据中挖掘的普遍含义的质量方面进行了严格的评估，但将它们与已知的普遍性知识库进行比较。 我们的研究结果将影响自然语言处理和语言学领域。 从研究方面来看，这项研究将在比我们打算研究的问题更广泛的问题中找到应用;特别是，在自然语言处理技术中使用语言共性将从根本上改变多语言在这一领域的解决方式。 从语言学的角度来看，这个项目的目标是对语言的共性有新的认识。 这不仅会影响语言类型学领域，还会影响濒危语言的研究和保护。 通过自动识别数据的类型特征和含义，记录濒危语言的过程可以变得更有效：导致这些语言知识的损失更小。