Barry L. Britt
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Interests
Barry L. Britt has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- James Kolpack
InRAD, LLC, 11020 Solway School Road, Suite 106, Knoxville, TN 37931
1 shared publication - Mary Ann Merrell
InRAD, LLC, 11020 Solway School Road, Suite 106, Knoxville, TN 37931
1 shared publication - Michael W. Berry
University of Tennessee, USA
1 shared publication - Murray Browne
Department of Electrical Engineering and Computer Science, University of Tennessee, 203 Claxton Complex, Knoxville, TN 37996-3450
1 shared publication
Latest Publications
- 4 January 2008
- Published by Wiley in Journal of the American Society for Information Science and Technology
- Vol. 59 (4) , 675-680
Document classification techniques for automated technology readiness level analysis
The overhead of assessing technology readiness for deployment and investment purposes can be costly to both large and small businesses. Recent advances in the automatic interpretation of technology readiness levels (TRLs) of a given technology can substantially reduce the risk and associated cost of bringing these new technologies to market. Using vector‐space information‐retrieval models, such as latent semantic indexing, it is feasible to group similar technology descriptions by exploiting the latent structure of term usage within textual documents. Once the documents have been semantically clustered (or grouped), they can be classified based on the TRL scores of (known) nearest‐neighbor documents. Three automated (no human curation) strategies for assigning TRLs to documents are discussed with accuracies as high as 86% achieved for two‐class problems.
- 4 January 2008
- Published by Wiley in Journal of the American Society for Information Science and Technology
- Vol. 59 (4) , 675-680
Document classification techniques for automated technology readiness level analysis
The overhead of assessing technology readiness for deployment and investment purposes can be costly to both large and small businesses. Recent advances in the automatic interpretation of technology readiness levels (TRLs) of a given technology can substantially reduce the risk and associated cost of bringing these new technologies to market. Using vector‐space information‐retrieval models, such as latent semantic indexing, it is feasible to group similar technology descriptions by exploiting the latent structure of term usage within textual documents. Once the documents have been semantically clustered (or grouped), they can be classified based on the TRL scores of (known) nearest‐neighbor documents. Three automated (no human curation) strategies for assigning TRLs to documents are discussed with accuracies as high as 86% achieved for two‐class problems.