Analogical Reasoning: An Algorithm Comparison for Natural Language Processing

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dc.contributor.authorCombs, Kara
dc.contributor.authorBihl, Trevor
dc.contributor.authorGanapathy, Subhashini
dc.contributor.authorStaples, Drue
dc.date.accessioned2021-12-24T17:28:19Z
dc.date.available2021-12-24T17:28:19Z
dc.date.issued2022-01-04
dc.description.abstractThere is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task. A significant limitation is the inability of AI to learn beyond its current comprehension. Analogical reasoning (AR), whereby learning by analogy occurs, has been proposed as one method to achieve this goal. Current AR models have their roots in symbolist, connectionist, or hybrid approaches which indicate how analogies are evaluated. No current studies have compared psychologically-inspired and natural language processing (NLP)-produced algorithms to one another; this study compares seven AR algorithms from both realms on multiple-choice word-based analogy problems. Assessment is based on selection of the correct answer, “correctness,” and their similarity score prediction compared to the “ideal” score, which is defined as the “goodness” metric. Psychologically-based models have an advantage based on our metrics; however, there is not a clear one-size-fits-all algorithm for all AR problems.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.161
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79493
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData, Text, and Web Mining for Business Analytics
dc.subjectanalogy
dc.subjectartificial intelligence
dc.subjectnatural language processing
dc.subjectreasoning
dc.subjecttext mining
dc.titleAnalogical Reasoning: An Algorithm Comparison for Natural Language Processing
dc.type.dcmitext

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