SBIR Phase II: Representation and Deep Learning for Free Text Applications

这个小型企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力来自于自由文本和其他结构化数据自动处理的增强能力。 这种方法的动机来自神经网络，反过来，它可以应用于神经建模和我们对大脑如何处理信息的理解。 在软件行业，商业创新继续围绕着网页的自动化处理，这在创建许多新公司方面发挥着关键作用。 因此，从自由文本自动决策的能力越来越重要。一种更好的方法来表示文本，以用于机器学习，这将在必须考虑句子结构的地方开辟新的功能。 这有可能导致新的创业企业，从而产生新的产品和服务。一个成功的项目将产生平台技术，可以提供一个实质性的竞争优势，利用它的公司，为消费者提供新的和更好的能力，并帮助推进国家的领先技术创新。这个小企业创新研究（SBIR）第二阶段项目旨在进一步开发新的方法来处理文本材料，使计算机可以更好地学习与自然语言相关的应用程序。 应用程序包括情感分析（为文本主体分配正面或负面视图），文档摘要以及使用来自许多类的固定集合的多个标签对文档进行分类。 该项目将进一步开发新技术以提高成绩，并开发转换文本和应用计算机化学习方法的原型组件。新技术使用单个高维向量（例如，1,000个数字的列表）同时表示单词和文档结构。 该项目旨在提高涉及文档、网页和其他文本的计算能力，并提供可应用于自动翻译、更好地计算机理解图像和基因组信息的新技术。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project derives from an enhanced capability for automated processing of free text and other structured data. Motivation for this approach comes from neural networks and, in turn, it has applications to neural modeling and our understanding of how the brain processes information. In the software industry, commercial innovation continues to revolve around automated processing of web pages, which plays a key role in creating many new companies. Therefore, the ability to automate decision-making from free text is increasing in importance. A better way to represent text for use with machine learning will open new capabilities wherever the structure of sentences must be taken into account. This has the potential to lead to new startup ventures, thereby resulting in new products and services. A successful project will result in platform technology that can provide a substantial competitive edge to companies that take advantage of it, provide new and better capabilities for consumers, and help advance the nation's lead in technological innovation.This Small Business Innovation Research (SBIR) Phase II project seeks to further develop new ways to process textual material so that computers can better learn applications related to natural language. Applications include sentiment analysis (assigning either positive or negative views to a body of text), summarization of documents, and classification of documents using multiple labels from a fixed set of many classes. The project will further develop new techniques to improve performance, and prototype components for transforming text and applying computerized learning methods. The new techniques represent words simultaneously with document structure using a single high-dimensional vector (for example, a list of 1,000 numbers). The project is aimed at improving computational capabilities involving documents, web pages, and other text as well as providing new techniques that can be applied to automated translation, better computer understanding of images, and genomic information.