III: Small: Active Learning of Language Models for Information Extraction

三：小：用于信息提取的语言模型的主动学习

• 批准号: 1016754
• 负责人: Douglas Downey
• 金额: $ 18.37万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016754&HistoricalAwards=false
• 关键词: III; Small; Active; Learning; Language

This project studies methods for extracting accurate knowledge bases from the Web. Fully-automated Web information extraction techniques are massively scalable, but have accuracy and coverage limitations. This proposal investigates how to improve automated extraction techniques by introducing carefully-selected human guidance. The proposed system continually extracts knowledge from the Web, along the way dynamically synthesizing and issuing queries to humans to increase the accuracy of the system's knowledge base and extractors.The approach extends the PI's previous work utilizing statistical language models (SLMs) for information extraction. Novel SLMs are investigated for unifying the extraction of relational data expressed in Web tables with extraction from free text. New active learning techniques utilize the models to identify "high-leverage" queries -- requesting, for example, textual extraction patterns that when retrieved from the Web yield thousands of novel extractions. The queries investigated are mostly amenable to non-experts, meaning that much of the human input can be acquired at scale via online mass-collaboration.The broader impact of this project lies in the potential for accurate Web extraction to radically improve Web search, allowing users to answer complicated questions by synthesizing information across multiple Web pages. In domains like medicine and biology, mining extracted knowledge bases could lead to important discoveries and novel therapies.Further information may be found at the project web page:http://wail.eecs.northwestern.edu/projects/activelms/index.html

本项目研究从Web中提取准确知识库的方法。 全自动Web信息提取技术具有很大的可扩展性，但具有准确性和覆盖范围的局限性。 该提案研究了如何通过引入精心选择的人类指导来改进自动提取技术。 所提出的系统不断地从Web上提取知识，沿着的方式动态地合成和发出查询人类，以增加系统的知识库和extractors.The方法的准确性扩展PI的以前的工作，利用统计语言模型（SLM）的信息提取。 研究了新的SLM，用于统一Web表中表示的关系数据的提取与从自由文本中提取。 新的主动学习技术利用模型来识别“高杠杆”查询-例如，请求文本提取模式，当从Web检索时，会产生数千个新的提取。 调查的查询大多是服从于非专家，这意味着大部分的人类输入可以通过在线大规模collaboration.The更广泛的影响，这个项目在于准确的Web提取，从根本上改善Web搜索的潜力，允许用户回答复杂的问题，通过综合多个网页上的信息。 在医学和生物学等领域，挖掘提取的知识库可能会导致重要的发现和新的疗法。http://wail.eecs.northwestern.edu/projects/activelms/index.html