SGER: Enriching Parser Output for Treebank Construction

SGER：丰富树库构建的解析器输出

• 批准号: 0527116
• 负责人: Anthony Kroch
• 金额: $ 4.99万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0527116&HistoricalAwards=false
• 关键词: SGER; Enriching; Parser; Output; Treebank

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.

在过去的十年中，用于语言学和自然语言处理（NLP）研究的树库的构建变得越来越广泛，从宾夕法尼亚树库和宾夕法尼亚-赫尔辛基历史英语解析语料库开始，现在扩展到其他语言的语料库，包括现代和历史语言。这些树库的建造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，输出在一定程度上不一致。本项目的产生是由于迫切需要进行树库工作，以便更迅速地产生更准确的产出。在美国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，应用最新开发的技术来丰富解析器输出，同时保持方法学的准确性。主要目标是快速部署树银行，但也要在更基本的层面上提高NLP技术的描述性充分性。这些更基本的改进应该对增加该技术在树银行本身以外的一系列应用中的能力和实际效用具有重要意义。这一建议的基本智力价值在于它将扩展当前语言学研究方法的力量，其更广泛的影响在于对自然语言技术的改进，如信息检索和机器翻译等实际应用。