CAREER: Robust Parsing for New Domains and Languages

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，它们在与训练样本不同的数据类型上表现不佳。这个CAREER项目的目标是增加基本NLP组件（语法解析器）的健壮性和覆盖率。具体来说，这个项目探索了适应方法来扩展一个标准的英语解析器，以处理不同的领域（如科学文献、电子邮件）和不同的语言（如中文）。考虑了三种类型的通信。首先，如果粗级对应在数据中是明确的（例如，双语文档），则可以推断出单词或短语级别的细粒度对应，并且可以使用半监督学习来跨推断对应转移领域知识。其次，如果对应关系是不精确的（例如，不同质量的多个翻译），不匹配的部分可以被识别和转换，以实现更紧密的映射。第三，如果对应关系是间接的，从非平行语料库中引出对应关系的方法可能是合适的。解析器适配将增加NLP应用的范围；例子包括：从医疗文件中挖掘数据和为非英语人士提供自动辅导。由于该项目旨在汇集几方面的研究，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、语法归纳和机器翻译等领域形成综合；实证方面为学生提供了一个舞台，以良好的科学方法磨练他们的技能。

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.