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

这个小企业创新研究阶段项目将实施和测试一种新的统计文本分类算法的可行性。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任务特定约束。该项目将评估这些算法的效率和有效性。可选的优化算法和可选的近似难以处理的量将基准。分类准确性将在公共文本分类数据集和来自操作文本分类用户的数据上进行评估。Ornarose公司将开发包括该算法在内的软件库，并将其许可给各行各业的软件供应商。对于那些文本分类不是核心竞争力的供应商来说，他们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很有吸引力的细分市场包括知识管理、新闻提醒、电子邮件/web过滤和数据挖掘。折扣许可将提供给学术机构，并计划在公开可用的文本分类数据集上发表非专有结果的学术出版物。

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.