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

SBIR 第二阶段：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: 1127464
• 负责人: Eric Koefoot
• 金额: $ 50万
• 依托单位: PublicRelay, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1127464&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Building; Flexible

This Small Business Innovation Research (SBIR) Phase II Project addresses the gap between the capabilities of today's semantic analysis 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 using products or systems based on semantic analysis technology utilizing extensive 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 connections between sentiment and topics. Users with high thresholds for accuracy thus turn to manual processes to either supplement or substitute for technology. Building upon Phase I work, the company will create and integrate a larger set of content processing modules and enhance a pluggable architecture to support quick insertion and testing of new modules in the content processing "pipeline." 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. Feedback to date suggests the product can substantially enhance the productivity and effectiveness of professionals in these fields and that it addresses a number of frustrating gaps in the marketplace.

该小型企业创新研究（SBIR）第二阶段项目旨在解决当今语义分析系统的能力与公共关系、外交事务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控和分析印刷品和网络报道中感兴趣的内容。当搜索结果量很大时，一些人使用基于语义分析技术的产品或系统对结果进行过滤、分类和评分，该语义分析技术利用大量的单词库、模式和上下文特定算法。然而，用户抱怨说，这些系统达不到预期的准确性，缺少修辞手段，如讽刺，讽刺，隐喻，双关语，并不正确地解释情感和主题之间的联系。因此，对准确性要求较高的用户转向手动流程，以补充或替代技术。在第一阶段工作的基础上，该公司将创建和集成一组更大的内容处理模块，并增强一个可插入的架构，以支持在内容处理“管道”中快速插入和测试新模块。“一旦商业化，该系统将使知识工作者能够更快地采用技术。在精度要求高的领域，对人工判断的需求限制了技术在搜索等离散领域的使用，而在随后的处理步骤中，分析师必须手动捕获，分类，评分，分析和报告输出。 迄今为止的反馈表明，该产品可以大大提高这些领域专业人员的生产力和效率，并解决了市场上一些令人沮丧的差距。