On helmholtz's principle for documents processing

Balinsky, Alexander; Balinsky, Helen; Simske, Steven J.

doi:10.1145/1860559.1860624

Cited by 20 publications

(31 citation statements)

References 6 publications

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“…Any 100-sequence of heads and tails can be generated with probability of (½)100 and Fig. 3 is generated where 1 represents heads and 0 represents tails (Balinsky et al, 2010). First sequence, s 1 is expectable for unbiased coin but second output, s 2 is highly unexpected.…”

Section: Helmholtz Principle From Gestalt Theory and Its Applicationsmentioning

confidence: 99%

“…This can be explained by using methods from statistical physics where we observe macro parameters but we don't know the particular configuration. For instance expectation calculations can be used for this purpose (Balinsky et al, 2010).…”

Section: Helmholtz Principle From Gestalt Theory and Its Applicationsmentioning

confidence: 99%

“…Based on the theories given above, new methods are developed for several related application areas including unusual behavior detection and information extraction from small documents (Dadachev et al, 2012), for text summarization (Balinsky et al, 2011b), defining relations between sentences using social network analysis and properties of small world phenomenon (Balinsky et al, 2011c) and rapid change detection in data streams and documents (Balinsky et al, 2010) and also for keyword extraction and rapid change detection (Balinsky et al, 2011a). These approaches make use of the fact that meaningful features and interesting events come into view if their deviations from randomness are very large.…”

Section: Helmholtz Principle From Gestalt Theory and Its Applicationsmentioning

confidence: 99%

“…The main novelty of our approach is the use of this class specific information in the smoothing process of the semantic kernel. The meaning values of terms are calculated according to the Helmholtz principle from Gestalt theory (Balinsky et al, 2010(Balinsky et al, , 2011a(Balinsky et al, , 2011b(Balinsky et al, , 2011c in the context of classes.…”

Section: Introductionmentioning

confidence: 99%

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A corpus-based semantic kernel for text classification by using meaning values of terms

Altınel

Ganiz

Di̇ri̇

2015

Engineering Applications of Artificial Intelligence

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a b s t r a c tText categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. Recently, there is an increased interest in the exploitation of background knowledge such as ontologies and corpus-based statistical knowledge in text categorization. It has been shown that, by replacing the standard kernel functions such as linear kernel with customized kernel functions which take advantage of this background knowledge, it is possible to increase the performance of SVM in the text classification domain. Based on this, we propose a novel semantic smoothing kernel for SVM. The suggested approach is based on a meaning measure, which calculates the meaningfulness of the terms in the context of classes. The documents vectors are smoothed based on these meaning values of the terms in the context of classes. Since we efficiently make use of the class information in the smoothing process, it can be considered a supervised smoothing kernel. The meaning measure is based on the Helmholtz principle from Gestalt theory and has previously been applied to several text mining applications such as document summarization and feature extraction. However, to the best of our knowledge, ours is the first study to use meaning measure in a supervised setting to build a semantic kernel for SVM. We evaluated the proposed approach by conducting a large number of experiments on well-known textual datasets and present results with respect to different experimental conditions. We compare our results with traditional kernels used in SVM such as linear kernel as well as with several corpus-based semantic kernels. Our results show that classification performance of the proposed approach outperforms other kernels.

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Section: Helmholtz Principle From Gestalt Theory and Its Applicationsmentioning

confidence: 99%

Section: Helmholtz Principle From Gestalt Theory and Its Applicationsmentioning

confidence: 99%

Section: Helmholtz Principle From Gestalt Theory and Its Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A corpus-based semantic kernel for text classification by using meaning values of terms

Altınel

Ganiz

Di̇ri̇

2015

Engineering Applications of Artificial Intelligence

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“…The corpus has been used in many text mining studies (see e.g. [17], [18], [19]) but according to our knowledge the present analysis is unique in its kind.…”

Section: B Text Mining Of Subjectivitymentioning

confidence: 99%

Subjects on objects in contexts: Using GICA method to quantify epistemological subjectivity

Honkela

Raitio

Lagus

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Abstract-A substantial amount of subjectivity is involved in how people use language and conceptualize the world. Computational methods and formal representations of knowledge usually neglect this kind of individual variation. We have developed a novel method, Grounded Intersubjective Concept Analysis (GICA), for the analysis and visualization of individual differences in language use and conceptualization. The GICA method first employs a conceptual survey or a text mining step to elicit from varied groups of individuals the particular ways in which terms and associated concepts are used among the individuals. The subsequent analysis and visualization reveals potential underlying groupings of subjects, objects and contexts. One way of viewing the GICA method is to compare it with the traditional word space models. In the word space models, such as latent semantic analysis (LSA), statistical analysis of word-context matrices reveals latent information. A common approach is to analyze term-document matrices in the analysis. The GICA method extends the basic idea of the traditional term-document matrix analysis to include a third dimension of different individuals. This leads to a formation of a third-order tensor of size subjects × objects × contexts. Through flattening into a matrix, these subject-object-context (SOC) tensors can again be analyzed using various computational methods including principal component analysis (PCA), singular value decomposition (SVD), independent component analysis (ICA) or any existing or future method suitable for analyzing highdimensional data sets. In order to demonstrate the use of the GICA method, we present the results of two case studies. In the first case, GICA of health-related concepts is conducted. In the second one, the State of the Union addresses by US presidents are analyzed. In these case studies, we apply multidimensional scaling (MDS), the self-organizing map (SOM) and Neighborhood Retrieval Visualizer (NeRV) as specific data analysis methods within the overall GICA method. The GICA method can be used, for instance, to support education of heterogeneous audiences, public planning processes and participatory design, conflict resolution, environmental problem solving, interprofessional and interdisciplinary communication, product development processes, mergers of organizations, and building enhanced knowledge representations in semantic web.

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Detecting Unusual Behaviour and Mining Unstructured Data

Balinsky

Simske

2016

UK Success Stories in Industrial Mathematics

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Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as prediction intervals, based on the assumption of data exchangeability. Typically, the construction of conformal predictions hinges on p-values. This paper, however, ventures down an alternative path, harnessing the power of e-test statistics to augment the efficacy of conformal predictions by introducing a BB-predictor (bounded from the below predictor).

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On helmholtz's principle for documents processing

Cited by 20 publications

References 6 publications

A corpus-based semantic kernel for text classification by using meaning values of terms

A corpus-based semantic kernel for text classification by using meaning values of terms

Subjects on objects in contexts: Using GICA method to quantify epistemological subjectivity

Detecting Unusual Behaviour and Mining Unstructured Data

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