Ideal WordsA Vector-Based Formalisation of Semantic Competence

被引:0
|
作者
Aurélie Herbelot
Ann Copestake
机构
[1] University of Trento,Department of Information Engineering and Computer Science, Center for Mind/Brain Sciences
[2] University of Cambridge,Department of Computer Science and Technology
来源
KI - Künstliche Intelligenz | 2021年 / 35卷
关键词
Formal semantics; Distributional semantics; Competence;
D O I
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中图分类号
学科分类号
摘要
In this theoretical paper, we consider the notion of semantic competence and its relation to general language understanding—one of the most sough-after goals of Artificial Intelligence. We come back to three main accounts of competence involving (a) lexical knowledge; (b) truth-theoretic reference; and (c) causal chains in language use. We argue that all three are needed to reach a notion of meaning in artificial agents and suggest that they can be combined in a single formalisation, where competence develops from exposure to observable performance data. We introduce a theoretical framework which translates set theory into vector-space semantics by applying distributional techniques to a corpus of utterances associated with truth values. The resulting meaning space naturally satisfies the requirements of a causal theory of competence, but it can also be regarded as some ‘ideal’ model of the world, allowing for extensions and standard lexical relations to be retrieved.
引用
收藏
页码:271 / 290
页数:19
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