CAREER: Robust Parsing for New Domains and Languages

职业：新领域和语言的稳健解析

• 批准号: 0745914
• 负责人: Rebecca Hwa
• 金额: $ 50万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0745914&HistoricalAwards=false
• 关键词: CAREER; Robust; Parsing; New; Domains

To facilitate linguistic communications, natural language processing (NLP) technologies must be applicable to different languages across different domains. A limitation of many NLP systems is that they do not perform as well on data types that diverge from their training examples. The objective of this CAREER project is to increase the robustness and coverage of a fundamental NLP component, the syntactic parser.Specifically, this project explores adaptation methods to extend a standard English parser for processing different domains (e.g., scientific literature, emails) and different languages (e.g., Chinese). Three types of correspondences are considered. First, if coarse-level correspondences are explicit in the data (e.g., bilingual documents), finer-grained correspondences at the word- or phrasal-level may be inferred, and semi-supervised learning may be used to transfer domain knowledge across the inferred correspondence.Second, if the correspondences are inexact (e.g., multiple translations of varying quality), the mis-matched portions may be identified and transformed to achieve a closer mapping. Third, if the correspondences are indirect, methods for inducing correspondences from non-parallel corpora may be appropriate.Parser adaptation stands to increase the range of NLP applications; examples include: data mining from medical documents and automatic tutoring for non-English speakers. As the project aims to bring together several strands of research, it offers ample research opportunities to graduate and undergraduate students. The algorithmic aspects encourage forming synthesis from areas of semi-supervised learning, relational data modeling, grammar induction, and machine translation; the empirical aspects afford students an arena to hone their skills in good scientific methodologies.

为了促进语言交流，自然语言处理（NLP）技术必须适用于不同领域的不同语言。 许多NLP系统的局限性在于，它们在与培训示例不同的数据类型上表现不佳。该职业项目的目的是提高基本NLP组件的鲁棒性和覆盖范围，即句法解析器。具体来说，该项目探讨了适应方法，以扩展用于处理不同域（例如，科学文献，电子邮件）和不同语言（例如，不同语言）（例如，中文）的标准英语解析器。 考虑了三种类型的对应关系。 First, if coarse-level correspondences are explicit in the data (e.g., bilingual documents), finer-grained correspondences at the word- or phrasal-level may be inferred, and semi-supervised learning may be used to transfer domain knowledge across the inferred correspondence.Second, if the correspondences are inexact (e.g., multiple translations of varying quality), the mis-matched portions may be identified and transformed to achieve近距离映射。 第三，如果对应关系是间接的，则可能适当诱导非并行语料库的对应关系的方法。Parser Adaptation将增加NLP应用程序的范围；示例包括：来自医疗文档的数据挖掘和非英语扬声器的自动辅导。 该项目旨在汇集一些研究，它为研究生和本科生提供了充足的研究机会。 算法方面鼓励从半监督学习，关系数据建模，语法诱导和机器翻译的领域形成合成；经验方面为学生提供了一个竞技场，可以在良好的科学方法论中磨练自己的技能。