SBIR Phase I: Statistical Text Categorization with Task-Specific Constraints

SBIR 第一阶段：具有特定任务约束的统计文本分类

• 批准号: 0232828
• 负责人: David Lewis
• 金额: $ 9.94万
• 依托单位: Ornarose, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0232828&HistoricalAwards=false
• 关键词: SBIR; Phase; Statistical; Text; Categorization

This Small Business Innovation Research Phase project will implement and test the feasibility of a new algorithm for statistical text categorization. This algorithm combines the ease and effectiveness of learning from examples, while incorporating task-specific constraints that currently require ad hoc rules. The project will evaluate the efficiency and effectiveness of these algorithms. Alternative optimization algorithms and alternative approximations to intractable quantities will be benchmarked. Categorization accuracy will be evaluated on public text categorization data sets and on data from operational text categorization users. Ornarose, Inc. will develop and license software libraries including this algorithm to software vendors in a variety of industries. Vendors for whom text categorization is not a core competency increasingly wish to support categorization in software that works with text data. Market niches where text categorization under task-specific constraints is compelling include knowledge management, news alerting, email/web filtering, and data mining. Discounted licenses will be provided for academic institutions and scholarly publication of nonproprietary results on publicly available text categorization data sets is planned.

这个小企业创新研究阶段项目将实现和测试一个新的算法的可行性统计文本分类。 该算法结合了从示例中学习的简单性和有效性，同时结合了当前需要特别规则的特定于任务的约束。 该项目将评估这些算法的效率和有效性。 替代的优化算法和替代近似棘手的数量将基准。 分类准确性将在公共文本分类数据集和操作文本分类用户的数据上进行评估。 Ornarose，Inc.将开发包括该算法的软件库，并将其授权给各个行业的软件供应商。 文本分类不是核心能力的供应商越来越希望在处理文本数据的软件中支持分类。 在特定任务约束下的文本分类的市场利基是引人注目的，包括知识管理，新闻警报，电子邮件/Web过滤和数据挖掘。 将为学术机构提供折扣许可，并计划在公开文本分类数据集上发表非专有结果。