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Semantic computing

About: Semantic computing is a research topic. Over the lifetime, 11188 publications have been published within this topic receiving 241331 citations.


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Journal ArticleDOI
TL;DR: The present paper shows how the extended theory can account for results of several production experiments by Loftus, Juola and Atkinson's multiple-category experiment, Conrad's sentence-verification experiments, and several categorization experiments on the effect of semantic relatedness and typicality by Holyoak and Glass, Rips, Shoben, and Smith, and Rosch.
Abstract: This paper presents a spreading-acti vation theory of human semantic processing, which can be applied to a wide range of recent experimental results The theory is based on Quillian's theory of semantic memory search and semantic preparation, or priming In conjunction with this, several of the miscondeptions concerning Qullian's theory are discussed A number of additional assumptions are proposed for his theory in order to apply it to recent experiments The present paper shows how the extended theory can account for results of several production experiments by Loftus, Juola and Atkinson's multiple-category experiment, Conrad's sentence-verification experiments, and several categorization experiments on the effect of semantic relatedness and typicality by Holyoak and Glass, Rips, Shoben, and Smith, and Rosch The paper also provides a critique of the Smith, Shoben, and Rips model for categorization judgments Some years ago, Quillian1 (1962, 1967) proposed a spreading-acti vation theory of human semantic processing that he tried to implement in computer simulations of memory search (Quillian, 1966) and comprehension (Quillian, 1969) The theory viewed memory search as activation spreading from two or more concept nodes in a semantic network until an intersection was found The effects of preparation (or priming) in semantic memory were also explained in terms of spreading activation from the node of the primed concept Rather than a theory to explain data, it was a theory designed to show how to build human semantic structure and processing into a computer

7,586 citations

Book
31 Oct 1995
TL;DR: The results of a true-false reaction-time task were found to support the hypothesis about memory organization that a canary is a bird and birds can fly.
Abstract: To ascertain the truth of a sentence such as “A canary can fly,” people utilize long-term memory. Consider two possible organizations of this memory. First, people might store with each kind of bird that flies (e.g., canary) the fact that it can fly. Then they could retrieve this fact directly to decide the sentence is true. An alternative organization would be to store only the generalization that birds can fly, and to infer that “A canary can fly” from the stored information that a canary is a bird and birds can fly. The latter organization is much more economical in terms of storage space but should require longer retrieval times when such inferences are necessary. The results of a true-false reaction-time task were found to support the latter hypothesis about memory organization.

2,671 citations

Journal ArticleDOI
Thomas Hofmann1
TL;DR: This paper proposes to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice, and results in a more principled approach with a solid foundation in statistical inference.
Abstract: This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed technique uses a generative latent class model to perform a probabilistic mixture decomposition. This results in a more principled approach with a solid foundation in statistical inference. More precisely, we propose to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis.

2,574 citations

Journal ArticleDOI
TL;DR: An automatic system for semantic role tagging trained on the corpus is described and the effect on its performance of various types of information is discussed, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty trace categories of the treebank.
Abstract: The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated.We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ''trace'' categories of the treebank.

2,416 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202349
202292
202115
202018
201918
201853