A Meta-learning Approach for Selecting between Response Automation Strategies in a Help-desk Domain

Yuval Marom, Ingrid Zukerman, Nathalie Japkowicz

We present a corpus-based approach for the automation of help-desk responses to users' email requests. Automation is performed on the basis of the similarity between a request and previous requests, which affects both the content included in a response and the strategy used to produce it. The latter is the focus of this paper, which introduces a meta-learning mechanism that selects between different information-gathering strategies, such as document retrieval and multi-document summarization. Our results show that this mechanism outperforms a random strategy-selection policy, and performs competitively with a gold baseline that always selects the best strategy.

Subjects: 13. Natural Language Processing; 12. Machine Learning and Discovery

Submitted: Apr 23, 2007


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