Term representation with Generalized Latent Semantic Analysis

Matveeva, Irina; Levow, Gina‐Anne; Farahat, Ayman; Royer, Christiaan

doi:10.1075/cilt.292.08mat

Cited by 28 publications

(14 citation statements)

References 23 publications

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“…Similar to activation updating of individual nodes, the strength of association reflects the log likelihood that two concepts cooccur in a document. Note that this is similar to models that utilize statistical language approaches (e.g., [5,7,11,13]) to calculate semantic relatedness from large text corpuses. However, in the current model, we assume that the user is naïve to the concepts to be found.…”

Section: Rational Updating In the Spreading Activation Networkmentioning

confidence: 96%

The microstructures of social tagging

2008

Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work

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This article presents a rational model developed under the distributed cognition framework that explains how social tags influence knowledge acquisition and adaptation in exploratory ill-defined information tasks. The model provides integrated predictions on the interactions among link selections, use and creation of tags, and the formation of mental categories. The model shows that the quality of tags not only influences search efficiency, but also the quality of mental categories formed during exploratory search. In addition, the model shows that aggregate regularities can be explained by microstructures of behavior that emerged from the adaptive assimilation of concepts and categories of multiple users through the social tagging system. The model has important implications on how collaborative systems could influence higher-level cognitive activities.

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Section: Rational Updating In the Spreading Activation Networkmentioning

confidence: 96%

The microstructures of social tagging

2008

Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work

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“…For example, Normalized Google Distance and PMI-IR are two search-engine-based measures of semantic similarity that estimate the relevance of one word to another as a function of hits that each of the words receives by itself and hits that the two words receive together. More intricate measures, like LSA and GLSA [11], can calculate the similarity of long sentences, paragraphs, or entire documents. Our technique for assessing the semantic relevancy between a query and the words on the screen relies on real-time accesses to publicly available web resources (e.g.…”

Section: Measures Of Semantic Similaritymentioning

confidence: 99%

Mapping semantic relevancy of information displays

Veksler

Gray

2007

CHI '07 Extended Abstracts on Human Factors in Computing Systems

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Semantic Relevancy Maps are a visual analytic technique for representing the distribution of semantic relevancy across an information display. The maps highlight the text areas of the display corresponding to the relevance of that text to user goals, with stronger highlights indicating higher degrees of relevance. Semantic Relevancy Maps were developed as a tool for high-fidelity computational cognitive models that search complex information displays in the same manner as humans. However, they offer the potential to be a standalone tool for quickly evaluating the spatial layout of information for designers or, more simply, for identifying the spatial location of sought-for information by any computer user.

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“…[5] compared GLSA and PMI on the same corpus (Corpus name is English Gigaworld collection (LDC)). Also it showed that GLSA outperforms PMI in capturing synonyms.…”

Section: Previous Workmentioning

confidence: 99%

A comparative study on Measure of Semantic Relatedness function

Emadzadeh

Nikfarjam

Muthaiyah

2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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The semantic analysis and context awareness in data mining can intensively increase results precision. In this research different semantic relatedness functions called "Measure of Semantic Relatedness (MSR)" are discussed and compared. We found that the quality and accuracy of MSRs are different when applied in various contexts. Here we compared several MSR algorithms using different corpuses and have analyzed the results.

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Term representation with Generalized Latent Semantic Analysis

Cited by 28 publications

References 23 publications

The microstructures of social tagging

The microstructures of social tagging

Mapping semantic relevancy of information displays

A comparative study on Measure of Semantic Relatedness function

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