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系统的一个限制是，它们不能很好地处理与其训练样本不同的数据类型。这个职业项目的目标是增加基本的自然语言处理组件-句法分析器的健壮性和覆盖率。具体地说，该项目探索了适配方法来扩展标准英语分析器，以处理不同的领域(例如，科学文献、电子邮件)和不同的语言(例如，中文)。我们考虑了三种类型的对应。首先，如果粗略对应关系在数据中是显式的(例如，双语文档)，则可以推断在单词或短语级别的细粒度对应关系，并且可以使用半监督学习来跨所推断的对应关系传递领域知识。第三，如果对应是间接的，从非平行语料库中归纳对应的方法可能是合适的。句法器适配将扩大自然语言处理的应用范围；例如：从医学文档中进行数据挖掘和为非英语使用者提供自动辅导。由于该项目旨在将几个研究方向结合在一起，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、语法归纳和机器翻译等领域形成综合；经验方面为学生提供了一个磨练良好科学方法技能的舞台。