Abstract
Natural language-based aids (e.g., intelligent cognitive assistants) that assist humans with various tasks and decisions, often need to recognize the user’s propensity (low-high) to elaborate on the task or decision, to ensure that the information provided matches the user’s thinking level. We conducted two qualitative studies of natural language usage in customers’ written product reviews (Study 1) and conversational transcripts of customer-store associate interactions (Study 2) to generate (Study 1) and validate (Study 2) four rules that can be employed to infer a user’s propensity for elaborative thinking. These include: consideration of multiple (2+) attributes/alternatives; detailed description (word count) about a single attribute/alternative; demonstration of specific knowledge (use of specific terms) about an attribute/alternative; and consideration of pros and cons about an attribute/alternative. Implications for natural language-based, intelligent cognitive assistants emerge as a result of this work.
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Acknowledgments
This material is based in part upon work supported by the National Science Foundation under Grant Numbers IIS-1527182 and IIS-1527302; the Alabama Agricultural Experiment Station; and the Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies acknowledged above.
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Chattaraman, V., Kwon, WS., Green, A., Gilbert, J.E. (2019). Inferring a User’s Propensity for Elaborative Thinking Based on Natural Language. In: Ayaz, H., Mazur, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 775. Springer, Cham. https://doi.org/10.1007/978-3-319-94866-9_32
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DOI: https://doi.org/10.1007/978-3-319-94866-9_32
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