Making computational sense of Montague's intensional logic

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 1977-12-01 DOI:10.1016/0004-3702(77)90025-X
Jerry R. Hobbs , Stanley J. Rosenschein
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Abstract

Montague's difficult notation and complex model theory have tended to obscure potential insights for the computer scientist studying Natural Language. Despite his strict insistence on an abstract model-theoretic interpretation for his formalism, we feel that Montague's work can be related to procedural semantics in a fairly direct way. A simplified version of Montague's formalism is presented, and its key concepts are explicated in terms of computational analogues. Several examples are presented within Montague's formalism but with a view toward developing a procedural interpretation. We provide a natural translation from intensional logic into lisp. This allows one to express the composition of meaning in much the way Montague does, using subtle patterns of functional application to distribute the meanings of individual words throughout a sentence. The paper discusses some of the insights this research has yielded on knowledge representation and suggests some new ways of looking at intensionality, context, and expectation.
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使蒙塔古的内涵逻辑具有计算意义
蒙塔古困难的符号和复杂的模型理论往往会模糊研究自然语言的计算机科学家的潜在见解。尽管蒙塔古严格坚持对其形式主义的抽象模型理论解释,但我们认为他的工作可以以一种相当直接的方式与程序语义学联系起来。本文提出了蒙太古形式主义的一个简化版本,并用计算类似物解释了其关键概念。在蒙太古的形式主义中提出了几个例子,但着眼于发展程序解释。我们提供了从内涵逻辑到口齿不清的自然翻译。这使得人们可以用蒙塔古的方式来表达意义的组成,使用微妙的功能应用模式将单个单词的意义分布在整个句子中。本文讨论了本研究在知识表示方面取得的一些见解,并提出了一些看待集约性、语境和期望的新方法。
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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