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

SBIR 第一阶段：自由文本应用的表示和深度学习

• 批准号: 1344944
• 负责人: Stephen Gallant
• 金额: $ 15万
• 依托单位: Textician, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344944&HistoricalAwards=false
• 关键词: SBIR; Phase; Representation; Deep; Learning

This Small Business Innovation Research (SBIR) Phase I project seeks to validate a new way to process textual material, so that computers can better learn applications related to natural language. A goal is to enable computer learning methods to take better account of parse structure in sentences, which is currently difficult. Two experimental prototypes will be constructed. One will focus upon automatically determining whether online posts about a company, brand or institution are positive or negative (sentiment analysis). This is difficult because sentence structure is important, and also because online posts can be informal, contain slang, etc. The second prototype finds the most important paragraph during a search, also taking sentence structure into account. Such paragraph-level information retrieval helps when extracting factual information from running text or web pages. The new method represents words simultaneously with parse structure using a single high-dimensional vector (for example, a list of 1,000 numbers). A successful SBIR project will ultimately improve machine learning applications for a wide range of tasks, including document retrieval, summarization, and automated translation. Moreover, the same techniques can be applied to represent any structured collection prior to machine learning, including images and genomic information. The broader impact/commercial potential of this project is to enhance capabilities 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 handle 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 will lead to new startups, and provide consumers with new products and services. A successful project will validate new technology that can give a huge competitive edge to companies that take advantage of it, provide new and better capabilities for consumers, and advance those countries, such as our own, whose economies benefit from new technological innovations.

这个小型企业创新研究（SBIR）第一阶段项目旨在验证一种处理文本材料的新方法，以便计算机可以更好地学习与自然语言相关的应用程序。 一个目标是使计算机学习方法能够更好地考虑句子中的解析结构，这在目前是困难的。 将建造两个实验原型。 其中一个将专注于自动确定关于公司，品牌或机构的在线帖子是积极的还是消极的（情感分析）。 这是困难的，因为句子结构很重要，也因为在线帖子可以是非正式的，包含俚语等。第二个原型在搜索过程中找到最重要的段落，也考虑到句子结构。 这样的段落级信息检索有助于从运行的文本或网页中提取事实信息。 新方法使用单个高维向量（例如，1，000个数字的列表）同时表示单词和解析结构。 一个成功的SBIR项目将最终改善机器学习应用程序的广泛任务，包括文档检索，摘要和自动翻译。 此外，相同的技术可以应用于在机器学习之前表示任何结构化集合，包括图像和基因组信息。该项目更广泛的影响/商业潜力是增强自由文本和其他结构化数据的自动处理能力。 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，处理自由文本的能力越来越重要。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。 这将导致新的创业公司，并为消费者提供新的产品和服务。一个成功的项目将验证新技术，使利用新技术的公司获得巨大的竞争优势，为消费者提供新的和更好的能力，并推动那些经济受益于新技术创新的国家，如我国。