RI-Small: Probabilistic Models for Structure Discovery in Text

RI-Small：文本结构发现的概率模型

• 批准号: 0915187
• 负责人: Noah Smith
• 金额: $ 44.99万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915187&HistoricalAwards=false
• 关键词: RI; Small; Probabilistic; Models; Structure

This project advances learning methods for obtaining linguistic knowledge from raw or nearly raw text; such knowledge constitutes a core component of natural language processing technology but is difficult to obtain, usually relying on expensive manual annotation of text data. Specifically, this project aims to automate some of the mechanical aspects of developing learning algorithms for linguistic structure (in part by using a empirical Bayesian framework to unify considerable past work by the PI and others), to enrich models with richer linguistic bias (particularly through lexicalization and integration of morphology and syntax), and to apply these techniques to new natural language processing problems (identifying boilerplate and quotation extraction). Another exciting dimension is learning from text collections in multiple languages (not necessarily including translations), which past work has shown can lead to better unsupervised learning. The project will lead to working systems, including generic tools applicable to many problems in natural language processing and machine learning. These tools will provide infrastructure for the PI's courses and will be publicly available to the research community. Research results will be published in leading journals and at major conferences. The project supports one primary graduate student and a post-doctoral researcher. Major impacts of this project will be improvements in the quality of rapidly ported natural language processing tools for new languages and text domains, as well as a deeper scientific understanding of natural language learning by machines.

该项目推进了从原始或接近原始文本中获取语言知识的学习方法;这些知识构成了自然语言处理技术的核心组成部分，但很难获得，通常依赖于昂贵的文本数据手动注释。 具体来说，这个项目的目的是自动化的一些机械方面的发展学习算法的语言结构（部分通过使用经验贝叶斯框架来统一PI和其他人过去的大量工作），以丰富语言偏见来丰富模型（特别是通过词汇化和词法与句法的整合），并将这些技术应用于新的自然语言处理问题（识别样板和引文提取）。 另一个令人兴奋的方面是从多种语言的文本集合中学习（不一定包括翻译），过去的工作表明这可以导致更好的无监督学习。 该项目将产生工作系统，包括适用于自然语言处理和机器学习中许多问题的通用工具。 这些工具将为PI的课程提供基础设施，并将公开提供给研究界。 研究成果将在主要期刊和主要会议上发表。 该项目资助一名小学研究生和一名博士后研究员。 该项目的主要影响将是提高用于新语言和文本领域的快速移植自然语言处理工具的质量，以及对机器自然语言学习的更深入的科学理解。