Web-Based Question Answering: A Decision-Making Perspective
This work addresses efficiency challenges for developers of question-answering systems, but it is incremental as it builds on existing methods like Bayesian analysis.
The paper tackled the problem of optimizing resource usage in web-based question answering systems by using probabilistic models and cost-benefit analyses to decide how many queries to issue to a search engine, resulting in improved decision-making for query allocation.
We describe an investigation of the use of probabilistic models and cost-benefit analyses to guide resource-intensive procedures used by a Web-based question answering system. We first provide an overview of research on question-answering systems. Then, we present details on AskMSR, a prototype web-based question answering system. We discuss Bayesian analyses of the quality of answers generated by the system and show how we can endow the system with the ability to make decisions about the number of queries issued to a search engine, given the cost of queries and the expected value of query results in refining an ultimate answer. Finally, we review the results of a set of experiments.