A Memory-based Theory of Verbal Cognition Export

@article{citeulike:767649, abstract = {The Syntagmatic Paradigmatic (SP) 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 (EM) algorithm. In this paper, 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.}, author = {Dennis, Simon}, citeulike-article-id = {767649}, citeulike-linkout-0 = {http://lsa.colorado.edu/~simon/SP%20Revised.pdf}, keywords = {algorithm-expectation-maximization, bayesian, categorization, language, lexicon, memory-long-term, memory-relational, memory-short-term, model-syntagmatic-paradigmatic, probability, sentence-processing, statistics, theory-of-string-edit}, posted-at = {2006-07-20 23:53:14}, priority = {2}, title = {A Memory-based Theory of Verbal Cognition}, url = {http://lsa.colorado.edu/~simon/SP%20Revised.pdf} }

TY - JOUR ID - citeulike:767649 L3 - citeulike-article-id:767649 N2 - The Syntagmatic Paradigmatic (SP) 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 (EM) algorithm. In this paper, 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. TI - A Memory-based Theory of Verbal Cognition KW - algorithm-expectation-maximization KW - bayesian KW - categorization KW - language KW - lexicon KW - memory-long-term KW - memory-relational KW - memory-short-term KW - model-syntagmatic-paradigmatic KW - probability KW - sentence-processing KW - statistics KW - theory-of-string-edit AU - Dennis, Simon UR - http://lsa.colorado.edu/~simon/SP%20Revised.pdf ER -