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

该项目研究了从网络中提取准确知识库的方法。 完全自动化的Web信息提取技术非常可扩展，但具有准确性和覆盖范围的限制。 该提案通过引入精心挑选的人类指导来调查如何改善自动提取技术。 提出的系统不断从网络中提取知识，并在动态综合并向人类发出查询的过程中，以提高系统知识库和提取器的准确性。该方法扩展了PI使用统计语言模型（SLMS）以进行信息提取的PI的工作。 研究了新型SLM，以统一在Web表中用自由文本提取的关系提取的关系数据。 新的主动学习技术利用模型来识别“高杠杆”查询 - 例如，从网络中检索到的文本提取模式，从而产生数千种新颖的提取。 调查的查询大多与非专家有关，这意味着可以通过在线群众群体进行大规模获取大量人类输入。该项目的更广泛的影响在于可能进行准确的Web提取以从根本上改善网络搜索，从而使用户可以通过跨多个网页综合信息来回答复杂的问题。 在医学和生物学等领域中，采矿提取的知识库可能会导致重要发现和新颖的疗法。FURTHER信息可以在项目网页上找到：http：//wail.eecs.northwestern.edu/projects/activelms/index.htex.html