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

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。使用引导技术可以大大增加可以提取的实体和关系类型的数量，以及能够创建新提取器的速度，2。通过使用自举来增加额外的训练特征和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，3。解决关于事实的来源、可信度和时间范围的元数据问题，特别关注基于Web数据纵向语料库的事实预期生命周期模型的构建。该项目的成果将是对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件集合转换为适合自动处理的正式数据库。这将代表数字图书馆和万维网的效用和社会效益的显著增强。项目成果将以出版物和可公开获取的信息提取代码的形式分发，以便提取者学习。

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.