SBIR/STTR Research Award

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

Award identifier ab64a720fe72bac8dc99f4476c9e1bf1bd5c2a632cd82ab2f0ca05e8541b1a58Awarding agency National Science Foundation

Award amount

$150,000

Program and phase

SBIR · Phase I

Award year

2014

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
TEXTICIAN, LLC
Proposal award date
2014-01-01
Contract end date
2014-12-31
UEI
N1GDCE2JLLH9
Solicitation number
Not reported
Topic code
EI

Research abstract

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.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

2 SBIR/STTR records share this UEI (2014–2015). Reported award amounts total $840,450. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: National Science Foundation

Agency intelligence

National Science Foundation

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