RI: SMALL: Statistical Linguistic Typology

本项目考虑了自然语言处理和语言类型学两种语言观的统一，我们的观点是类型信息既有助于解决现实世界中的自然语言处理问题，又可以从语言数据中自动派生出来。本研究首先探讨了如何利用语言学知识来提高低密度语言的依存句法分析和机器翻译等问题的性能，但是我们的统计模型浪费了大量的时间来探索一个过于庞大的假设空间：现实语法的空间比所有语法的空间都要小得多。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的问题。这项研究的结果是：(A)改进了自然语言处理困难问题的统计模型；(B)从数据中自动得出了更大的类型学共性库。我们的结果是在原始语言处理任务上进行的经验性评估，并根据从数据中挖掘的普遍含义的质量来评估，但将它们与已知的共性储存库进行比较。我们的结果将对自然语言处理和语言学领域产生影响。在研究方面，这项研究将发现比我们打算研究的问题更广泛的应用；特别是，语言共性在自然语言处理技术中的使用将从根本上改变这一领域解决多语言问题的方式。从语言学的角度来看，这个项目的目标是对语言共性有新的认识。这不仅应该影响类型学领域，而且还应该影响对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更有效率：导致对这些语言的知识损失较小。

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.