CAREER Hybrid methods for acquisition and tuning of lexical information

用于自然语言处理（NLP）的广泛覆盖的字典和本体很难手工创建和维护，而且成本很高。因此，需要从分布信息中学习它们，例如可以从未标记或稀疏标记的文本语料库中获得。许多语言学和心理语言学理论是分布的，但比以前的NLP方法更强调局部邻域结构。成功的可视化技术，如上下文关键字，也依赖于邻域结构的保存。类似的重点也出现在新兴的数据简化技术中，例如LLE和最小切算法，该项目正在研究其在语言数据中的应用。虽然该项目的直接目标是更好地理解词汇调优和获取，但由此产生的词典、本体和映射技术有可能帮助信息专业人员（如图书管理员、翻译人员、专利审查员和律师助理研究人员）浏览语料库，理解他们所看到的数据的重要性，并将从数据中获得的见解纳入他们的工作实践中。PI和他的学生还将计算语言学整合到语言学系的本科课程中，并创建了新的课程，主要是为了吸引人文专业的学生，以便为这些学生提供新的选择，以满足大学广度要求的科学、数学和定量组成部分。

Broad coverage dictionaries and ontologies for natural language processing (NLP) are difficult and costly to create and maintain by hand. It is therefore desirable to learn them from distributional information, such as can be obtained from unlabeled or sparsely labeled text corpora. Many linguistic and psycholinguistic theories are distributional, but emphasize local neighborhood structure more than do previous NLP approaches. Successful visualization techniques such as keyword-in-context also rely on the preservation of neighborhood structure. A similar emphasis is present in emerging techniques for data reduction, such as LLE and min-cut algorithms, whose application to language data the project is investigating.While the immediate goal of the project is to gain a better understanding of lexical tuning and acquisition, the resulting dictionaries, ontologies and mapping techniques have the potential to help information professionals (such as librarians, translators, patent examiners and paralegal researchers) to navigate through corpora, to understand the significance of the data that they see, and to incorporate insights derived from the data into their working practice.The PI and his students are also integrating computational linguistics into the undergraduate curriculum of the Department of Linguistics and creating new courses designed primarily to appeal to students majoring in the humanities in order to offer such students fresh options in meeting the scientific, mathematical, and quantitative components of the university's breadth requirement.