连接认知科学与NLP，揭示类比推理的深层机制。
Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research
- 从认知科学提炼类比推理核心过程，映射到NLP任务
- 强调关系理解优于实体相似性，提升文本语义深度
- 为关系型任务提供新视角，适合语言理解研究者
类比推理是人类认知的核心能力。本文系统梳理了认知科学中关于类比推理过程的关键理论，并将其与自然语言处理（NLP）研究相衔接。尽管这些认知过程可自然对应到NLP概念，但当前研究普遍缺乏认知视角。我们进一步指出，这些理念对多个非类比直接相关的主流NLP挑战也具有重要意义，有助于推动研究从依赖实体级相似性转向更深层次的关系理解。
原文摘要 · Abstract (English)
Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to analogy solving. This may guide researchers to better optimize relational understanding in text, as opposed to relying heavily on entity-level similarity.
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