SGER: Enriching Parser Output for Treebank Construction

在过去的十年中，用于语言和自然语言处理（NLP）研究的树库的构建变得越来越普遍，从Penn Treebank和Penn-Helsinki Parsed Corpora of Historical English开始，现在扩展到其他语言的语料库，包括现代和历史。在构建这些树库中使用的方法是部分自动化的，但需要大量的人工校正，导致生产速度缓慢，产出存在一定程度的不一致性。本项目的产生是因为迫切需要树库工作，以产生更准确的输出，并更快地这样做。在国家科学基金会的支持下，Anthony Kroch博士，Seth Kulick博士和Mitch Marcus博士将改进语料库构建的自动化工具，应用最近开发的技术来丰富解析器输出，同时保持括号的准确性。主要目标是快速部署树库，但也要在更基础的层面上提高NLP技术的描述性。这些更根本的改进应该对提高该技术在树库本身之外的一系列应用中的能力和实际效用具有重要意义。这一建议的基本知识价值在于，它将扩大目前语言研究方法的力量，其更广泛的影响在于设想改进自然语言技术，以实现信息检索和机器翻译等实际应用。

The construction of treebanks for linguistic and natural language processing (NLP) research has become more and more widespread over the past decade, beginning with the Penn Treebank and the Penn-Helsinki Parsed Corpora of Historical English and now extending to corpora of other languages, both modern and historical. The methods used in the construction of these treebanks are partially automated but require extensive manual correction, leading to a slow rate of production and a certain level of inconsistency in the output. The present project arises out of the urgent need for treebanking efforts to produce more accurate output and to do so more rapidly. With National Science Foundation support, Dr. Anthony Kroch, Dr. Seth Kulick and Dr. Mitch Marcus will improve the automated tools for corpus construction, applying recently developed techniques to enrich parser output while preserving bracketing accuracy. The primary goal is rapid deployment for treebanking but also to improve the descriptive adequacy of NLP technology on a more fundamental level. These more fundamental improvements should have important implications for increasing the power and practical utility of the technology in a range of applications beyond treebanking itself. The fundamental intellectual merit of this proposal is that it will extend the power of current methods of linguistic research and its broader impacts lie in the envisaged improvements to natural language technology for such practical applications as information retrieval and machine translation.