SBIR Phase I: Building a Flexible, Technology Adaptive Architecture to Support Processing of Content by Knowledge Workers

SBIR 第一阶段：构建灵活的技术自适应架构以支持知识工作者的内容处理

• 批准号: 1013935
• 负责人: Eric Koefoot
• 金额: $ 15万
• 依托单位: PublicRelay, Inc.
• 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: 2010-07-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1013935&HistoricalAwards=false
• 关键词: SBIR; Phase; Building; Flexible; Technology

This Small Business Innovation Research (SBIR) Phase I Project addresses the gap between the capabilities of today's natural language processing systems and the accuracy requirements of knowledge workers (analysts and researchers) in language-sensitive fields such as public relations, foreign affairs, and crisis management. Knowledge workers in many organizations monitor and analyze print and web coverage for content of interest. When the volume of search results is large, some filter, classify and score the results with Natural Language Processing (NLP) systems using complex libraries of words, patterns, and context-specific algorithms. However, users complain that these systems fall short of desired accuracy, missing rhetorical devices such as irony, sarcasm, metaphors, double entendre, and improperly interpreting references. Users with high thresholds for accuracy thus turn to manual processes to either supplement or substitute for technology. This project will test a prototype architecture allowing rapid insertion, testing, and adaptation of text analysis algorithms and a workflow process efficiently integrating human review judgment. Once commercialized, the system will enable more rapid adoption of technology by knowledge workers. In fields with high accuracy requirements, the need for human judgment has constrained technology use to discrete areas like search, while in subsequent processing steps, analysts must manually capture, classify, score, analyze, and report on the output.

这个小型企业创新研究 (SBIR) 第一阶段项目旨在解决当今自然语言处理系统的功能与公共关系、外交和危机管理等语言敏感领域的知识工作者（分析师和研究人员）的准确性要求之间的差距。 许多组织中的知识工作者监视和分析印刷品和网络报道以获取感兴趣的内容。当搜索结果量很大时，一些搜索结果会使用复杂的单词、模式和上下文特定算法库，通过自然语言处理 (NLP) 系统对结果进行过滤、分类和评分。然而，用户抱怨这些系统达不到预期的准确性，缺少反讽、挖苦、隐喻、双关语等修辞手段，并且对引用的解释不正确。因此，对准确性要求较高的用户会转向手动流程来补充或替代技术。 该项目将测试一个原型架构，该架构允许快速插入、测试和调整文本分析算法以及有效集成人工审核判断的工作流程。一旦商业化，该系统将使知识工作者能够更快地采用技术。在准确性要求较高的领域，对人类判断的需求限制了技术在搜索等离散领域的使用，而在后续处理步骤中，分析人员必须手动捕获、分类、评分、分析和报告输出。