Abstract
As a result of the continuously growing volume of information available, browsing and querying of textual information in search of specific facts is currently a tedious task exacerbated by a reality where data presentation very often does not meet the needs of users. To satisfy these ever-increasing needs, we have designed an solution to provide an adaptive and intelligent solution for the automatic answer of multiple-choice questions based on the concept of mutual information. An empirical evaluation over a number of general-purpose benchmark datasets seems to indicate that this solution is promising.
This manuscript is an extended version of Ref. [22].
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Acknowledgements
We would like to thank the anonymous reviewers for their helpful suggestions to improve this work. This research has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH.
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Martinez-Gil, J., Freudenthaler, B., Tjoa, A.M. (2019). A General Framework for Multiple Choice Question Answering Based on Mutual Information and Reinforced Co-occurrence. In: Hameurlain, A., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLII. Lecture Notes in Computer Science(), vol 11860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60531-8_4
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