Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions
Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions
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DOI：
10.1177/1745691619861372
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发表时间：
2019-09-10
影响因子：
12.6
通讯作者：
Marelli, Marco
中科院分区：
文献类型：
--
作者：
Guenther, Fritz;Rinaldi, Luca;Marelli, Marco
将意义表示为高维数值向量的模型，如潜在语义分析（LSA），超空间语言模拟（HAL），聚合语言环境的绑定编码（BEAGLE），主题模型，全局向量（GloVe）和word2vec，已被引入作为人类语义表示的非常强大的机器学习代理，并在过去20年中出现了爆炸性的流行。然而，尽管它们在认知科学中取得了相当大的进步和传播，但人们可以观察到与充分介绍和理解它们的一些特征相关的问题。事实上，当从认知的角度来审视这些模型时，心理学文献中往往会出现一些毫无根据的论点。在这篇文章中，我们回顾了这些论点中最常见的，并讨论了（a）这些模型在实现层面上究竟代表了什么，以及它们作为认知理论的可扩展性，（B）它们如何处理意义的各个方面，如一词多义或组合性，以及（c）它们如何与具身认知和扎根认知的争论有关。我们确定了常见的误解，产生的不完整的描述，过时的参数，以及理论和模型的实现之间的不明确的区别。我们澄清并修正了这些观点，为今后语义表示的向量模型的研究和讨论提供了理论基础。
Models that represent meaning as high-dimensional numerical vectors-such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec-have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation.