Analogical Reasoning: An Algorithm Comparison for Natural Language Processing

Combs, Kara
Bihl, Trevor
Ganapathy, Subhashini
Staples, Drue
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There 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.
Data, Text, and Web Mining for Business Analytics, analogy, artificial intelligence, natural language processing, reasoning, text mining
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