SBIR Phase I: ScenarioNet: Customizable Deep Information Extraction

SBIR 第一阶段：ScenarioNet：可定制的深度信息提取

• 批准号: 9960024
• 负责人: Milton Palmer
• 金额: $ 10万
• 依托单位: Naftware Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2000-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9960024&HistoricalAwards=false
• 关键词: SBIR; Phase; ScenarioNet; Customizable; Deep

This Small Business Innovation Research Phase I project from Naftware, Inc. addresses the need for better information search methods. Recent advances in computational linguistics (event semantics) present opportunities for major productivity enhancements for American knowledge workers by enabling the development of ScenarioNet, which automatically learns to extract deep event information from text. A major deficiency in current information extraction (IE) systems is that training examples must be painstakingly annotated by human experts. In ScenarioNet, examples are automatically extracted, ranked, and kept or discarded in rapid user-feedback cycles, dramatically streamlining training the system for new or revised domains. To enable a 'deeper' level of event information extraction, ScenarioNet incorporates a statistical full parser, event models, event builder (with sub-event merging), relationship models (extracting multiple relationships from a single phrase or sentence), scenario models, and scenario builder (with event merging algorithms). For deep IE, events themselves must be placed in context of related events: ScenarioNet's hierarchical event and scenario models include representations of causal, temporal, and structural relationships. Event merging algorithms utilize coreference resolution techniques, including discourse-level and cross-document coreference, to recognize multiple elements of events and how events combine into scenarios. ScenarioNet eases cross-domain portability and enables deep event, relationship, and scenario information extraction.A robust, easily customizable event, relationship, and scenario information extraction system has strong commercial potential in such industries as defense, intelligence, insurance (review of applications and claims), healthcare, financial services, legal services, business intelligence gathering, all levels of government (review of applications and reports) and engineering (to keep up with new developments). Naftware's proffered technology, ScenarioNet, removes the barriers to wide commercialization of IE by cutting the customization effort (cross-domain portability) and by incorporating deeper semantics into its object-oriented extraction models and templates.

Naftware，Inc.的这个小型企业创新研究第一阶段项目解决了对更好的信息搜索方法的需求。计算语言学(事件语义学)的最新进展使ScenarioNet的开发成为可能，为美国知识工作者提供了显著提高生产率的机会。ScenarioNet自动学习从文本中提取深层事件信息。当前信息抽取(IE)系统的一个主要缺陷是训练样本必须由人类专家煞费苦心地标注。在ScenarioNet中，示例被自动提取、排序，并在快速的用户反馈周期中保留或丢弃，从而极大地简化了针对新领域或修订后的领域的系统培训。为了实现更深层次的事件信息提取，ScenarioNet集成了统计完整解析器、事件模型、事件构建器(使用子事件合并)、关系模型(从单个短语或句子中提取多个关系)、场景模型和场景构建器(使用事件合并算法)。对于深度IE，事件本身必须放在相关事件的上下文中：ScenarioNet的分层事件和场景模型包括因果关系、时间关系和结构关系的表示。事件合并算法利用共指解析技术，包括语篇级别和跨文档共指，来识别事件的多个元素以及事件如何组合成场景。ScenarioNet简化了跨域可移植性，并支持深度事件、关系和场景信息提取。强大的、可轻松定制的事件、关系和场景信息提取系统在国防、情报、保险(审查申请和索赔)、医疗保健、金融服务、法律服务、商业情报收集、各级政府(审查申请和报告)和工程(以跟上新发展)等行业具有强大的商业潜力。Naftware提供的技术ScenarioNet通过减少定制工作(跨域可移植性)并将更深的语义融入其面向对象的提取模型和模板，消除了IE广泛商业化的障碍。