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Details of Grant 

EPSRC Reference: EP/R005567/1
Title: Virtual Investment Researcher
Principal Investigator: Briscoe, Professor EJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Laboratory
Organisation: University of Cambridge
Scheme: Technology Programme
Starts: 01 January 2017 Ends: 31 December 2017 Value (£): 84,693
EPSRC Research Topic Classifications:
Artificial Intelligence Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:  
Summary on Grant Application Form
SEWA will gather relevant information sources through a combination of searches on the company, relevant websites, and public and internal databases. This information may be in textual or numerical form represented in HTML, Word, PDF, Excel or SQL formats. Semi-automating this process requires the development of a system that can take a company name as input, search the relevant websites, intranets, and databases, handling the different formats, and return a superset of potentially relevant sources from which the researcher can quickly select. In order to exploit techniques from the field of information retrieval, such as query expansion and relevance feedback to ensure all sources are found efficiently, the research team will need to develop a bespoke solution built on an open source platform. To achieve the full functionality required for the application, this solution will need to go beyond the currently available technology by, for example, being able to accurately index and extract text sentences and paragraphs from PDF documents.

Text analytics or information extraction will require the system to learn contextual patterns encoding relevant types of information on the basis of the links between text regions and report content. To customise such technology to the company report application will require the researchers to deploy either open source platforms or flexible commercial toolkits, for example for named entity recognition or relation extraction and the formatting of output in XML. The researchers provide input to the application developers, for example to optimise the reliability and accuracy of information aggregation.

Automation of text generation will be undertaken using the structured XML content extracted. The university research team will develop and apply appropriate algorithms which are able to generate natural language, and produce pre-defined text according to rules set up by the business team. The researchers will also provide input to the software development team on the integration of the selected algorithms and toolkits into the CUI.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
Date Materialised
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Organisation Website: http://www.cam.ac.uk