A memory-based theory of verbal cognition
A memory-based theory of verbal cognition
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DOI：
10.1207/s15516709cog0000_9
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发表时间：
2005-03-01
影响因子：
2.5
通讯作者：
Dennis, S
中科院分区：
文献类型：
--
作者：
Dennis, S
组合聚合模型是一种分布式的、基于记忆的言语加工模型。它建立在对串编辑理论的贝叶斯解释的基础上，将言语认知的控制描述为从顺序和关系长时记忆中提取组合和聚合限制，并在工作记忆中解决这些限制。使用期望最大化算法的一种版本直接从文本中提取词汇信息。本文从句子加工、语义分类和评分、短时连续回忆、类比推理和逻辑推理等几个方面对该模型进行了描述和说明。随后，该模型被用于回答关于从互联网上获取的网球新闻文章语料库的问题。该模型的成功表明，从自然出现的文本中提取命题信息是可能的，而不需要使用语法、定义一组启发式规则或指定一组先验的语义角色。
The syntagmatic paradigmatic model is a distributed, memory-based account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational long-term memory and the resolution of these constraints in working memory. Lexical information is extracted directly from text using a version of the expectation maximization algorithm. In this article, the model is described and then illustrated on a number of phenomena, including sentence processing, semantic categorization and rating, short-term serial recall, and analogical and logical inference. Subsequently, the model is used to answer questions about a corpus of tennis news articles taken from the Internet. The model's success demonstrates that it is possible to extract propositional information from naturally occurring text without employing a grammar, defining a set of heuristics, or specifying a priori a set of semantic roles.