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

该项目研究从网络中提取准确知识库的方法。 全自动 Web 信息提取技术具有大规模可扩展性，但存在准确性和覆盖范围限制。 该提案研究了如何通过引入精心选择的人工指导来改进自动提取技术。 所提出的系统不断地从网络中提取知识，同时动态地合成并向人类发出查询，以提高系统知识库和提取器的准确性。该方法扩展了 PI 之前利用统计语言模型 (SLM) 进行信息提取的工作。 研究新颖的 SLM 来统一 Web 表中表达的关系数据的提取和自由文本的提取。 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，当从网络检索时，会产生数千个新颖的提取。 所调查的查询大多适合非专家，这意味着大部分人类输入可以通过在线大规模协作大规模获取。该项目的更广泛影响在于准确的网络提取有可能从根本上改善网络搜索，从而允许用户通过综合多个网页的信息来回答复杂的问题。 在医学和生物学等领域，挖掘提取的知识库可能会带来重要的发现和新颖的疗法。更多信息可以在项目网页上找到：http://wail.eecs.northwestern.edu/projects/activelms/index.html