Original Contribution: Stacked generalization
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IBM Journal of Research and Development
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IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Structured data and inference in DeepQA
IBM Journal of Research and Development
Learning to rank for robust question answering
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Introduction to "This is Watson"
IBM Journal of Research and Development
Question analysis: how watson reads a clue
IBM Journal of Research and Development
Finding needles in the haystack: search and candidate generation
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Relation extraction and scoring in DeepQA
IBM Journal of Research and Development
Structured data and inference in DeepQA
IBM Journal of Research and Development
Special questions and techniques
IBM Journal of Research and Development
Identifying implicit relationships
IBM Journal of Research and Development
In the game: the interface between Watson and Jeopardy!
IBM Journal of Research and Development
A phased ranking model for question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.