Collaborative Research: Semantic Entity and Relation Extraction from Web-Scale Text Document Collections

协作研究：从网络规模文本文档集合中提取语义实体和关系

• 批准号: 0308024
• 负责人: Steven Abney
• 金额: $ 33.88万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-15 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0308024&HistoricalAwards=false
• 关键词: Collaborative; Research; Semantic; Entity; Relation

This project addresses current limitations in automatic information extraction technology. Specific objectives are to:1. use bootstrapping techniques to greatly increase the number of types of entities and relations that can be extracted and the rate at which one is able to create new extractors,2. improve the performance of supervised training for entity and relation extractors by using bootstrapping to add additional training features and by applying new supervised learning techniques, including new perceptron and discriminative training techniques,3. address meta-data issues of provenance, confidence, and temporal extent of facts, focussing particularly on the construction of a model of the expected lifetime of facts based on a longitudinal corpus of Web data.The outcome of the project will be scientific understanding and technology for automatic information extraction from free text, making it possible to convert large document collections into formal databases suitable for automated processing. This will represent a significant enhancement in the utility and societal benefit of digital libraries and the World Wide Web. Project results will be disseminated in the form of publications and publicly available code for information extraction and learning of extractors.

该项目解决了当前自动信息提取技术的局限性。 具体目标是：1.使用自举技术来极大地增加可以提取的实体和关系的类型的数量以及能够创建新提取器的速率，2.通过使用自举来添加额外的训练特征，并通过应用新的监督学习技术，包括新的感知器和判别训练技术，提高实体和关系提取器的监督训练性能。解决事实的来源、可信度和时间范围等元数据问题，特别侧重于根据网络数据的纵向语料库构建事实的预期寿命模型。该项目的成果将是从自由文本中自动提取信息的科学理解和技术，从而有可能将大型文档集转换为适合自动处理的正式数据库。 这将大大提高数字图书馆和万维网的实用性和社会效益。 项目成果将以出版物和公开代码的形式传播，供信息提取和提取者学习。