This paper appears in:
IBM Journal of Research and Development
Date of Publication:
May-June 2012
Author(s):
Gondek, D. C.
IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
Lally, A.
;
Kalyanpur, A.
;
Murdock, J. W.
;
Duboue, P. A.
;
Zhang, L.
;
Pan, Y.
;
Qiu, Z. M.
;
Welty, C.
Volume: 56,
Issue: 3.4
On Page(s):
14:1
-
14:12
Product Type:
Journals & Magazines
Abstract
The final stage in the IBM DeepQA pipeline involves ranking all candidate answers according to their evidence scores and judging the likelihood that each candidate answer is correct. In DeepQA, this is done using a machine learning framework that is phase-based, providing capabilities for manipulating the data and applying machine learning in successive applications. We show how this design can be used to implement solutions to particular challenges that arise in applying machine learning for evidence-based hypothesis evaluation. Our approach facilitates an agile development environment for DeepQA; evidence scoring strategies can be easily introduced, revised, and reconfigured without the need for error-prone manual effort to determine how to combine the various evidence scores. We describe the framework, explain the challenges, and evaluate the gain over a baseline machine learning approach.
Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.
Index Terms
Index Terms are available to subscribers and IEEE members.
ISSN : 0018-8646
Digital Object Identifier : 10.1147/JRD.2012.2188760
Date of Current Version :
Thu Apr 05 00:00:00 EDT 2012
Issue Date :
May-June 2012
Sponsored by :
IBM
Available to subscribers and IEEE members.
Available to subscribers and IEEE members.
