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并测试统计文本分类新算法的可行性。该算法结合了从示例中学习的易用性和有效性，同时纳入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效果。对难以处理的数量的替代优化算法和替代近似将进行基准测试。分类准确率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，Inc.将开发包括该算法的软件库，并将其授权给各种行业的软件供应商。对于那些文本分类不是核心能力的供应商来说，越来越多的供应商希望在使用文本数据的软件中支持分类。在特定于任务的约束下进行文本分类的利基市场包括知识管理、新闻警报、电子邮件/网页过滤和数据挖掘。将向学术机构提供打折的许可证，并计划在公开可用的文本分类数据集上以学术形式发布非专有结果。

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.