Graph-Theoretic Techniques for Web Content Mining

Schenker, Adam; Kandel, Abraham; Bunke, Horst; Last, Mark

doi:10.1142/9789812569455

Cited by 56 publications

(73 citation statements)

References 61 publications

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“…Friburger et al (2002) found that the combined use of a "named entities" vector and an "allwords" vector with an increased weight to the entities vector had the best overall performance. Schenker et al (2005) performed text clustering and classifications tasks using graph representation models and graph-based similarity measures. They also introduced graph edit distance metrics, The proposed approach builds on the metrics introduced by (Giannakopoulos and Karkaletsis, 2009) using the findings of (Friburger et al, 2002).…”

Section: Related Workmentioning

confidence: 99%

A Graph-based Text Similarity Measure That Employs Named Entity Information

Tsekouras¹,

Varlamis

Giannakopoulos

2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities' information extracted from the texts and the ngram graphs' model for representing documents. Using OpenCalais as a namedentity recognition service and the JIN-SECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means). The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.

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Section: Related Workmentioning

confidence: 99%

A Graph-based Text Similarity Measure That Employs Named Entity Information

Tsekouras¹,

Varlamis

Giannakopoulos

2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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“…A ferramenta utiliza um algoritmo que realiza uma análise estatística dos termos presentes no texto e os seleciona a partir do valor absoluto de sua ocorrência. Esse modelo de mineração textual, denominado n-simple distance, considera também as relações de proximidade entre os componentes do texto, ligando cada termo estatisticamente relevante a N subsequentes palavras também relevantes (SCHENKER, 2003).…”

Section: A Ferramenta Sobekunclassified

Emprego de um software baseado em mineração de texto e apresentação gráfica multirrepresentacional como apoio à aprendizagem de conceitos científicos a partir de textos no Ensino Fundamental

Costa

Reategui

Epstein

et al. 2017

Ciênc. educ. (Bauru)

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This work presents two independent studies that aim to investigate how a tool based on text mining and multi-representational graphical presentation (SOBEK) can contribute to the process of constructing scientific concepts from text by elementary school students. The studies were conducted in a quasi-experimental design with application of pre-test and post-tests with non-equivalent control groups. Refutational texts from published researches were used, as well as the corresponding performance tests about each text. The concepts covered are energy and the particulate nature of matter. Performed in a municipal school in Southern Brazil, the studies included two classes of 8th grade students. Statistical analysis of scores performed by students in tests show significant improvements in student´s performance that used the SOBEK tool as support for the study of text. More detailed studies are needed to identify the occurrence and extent of possible changes in students´ conceptual structure supported by the proposed approach.

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“…A graph-based version of the classic k -means clustering algorithm has been presented in [5]. The main differences consist in the distance and the centroid computation.…”

Section: The Graph-based K -Means Clustering Algorithmmentioning

confidence: 99%

“…For instance, in [4] the clustering of shock trees using the tree edit distance was introduced. Finally, the extension of the k -means clustering algorithm to graph based representations was introduced in [5].…”

Section: Introductionmentioning

confidence: 99%

Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph

Ferrer

Valveny

Serratosa

et al. 2009

Computer Analysis of Images and Patterns

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Abstract. In this paper we propose the application of the generalized median graph in a graph-based k -means clustering algorithm. In the graph-based k -means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.

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Graph-Theoretic Techniques for Web Content Mining

Cited by 56 publications

References 61 publications

A Graph-based Text Similarity Measure That Employs Named Entity Information

A Graph-based Text Similarity Measure That Employs Named Entity Information

Emprego de um software baseado em mineração de texto e apresentação gráfica multirrepresentacional como apoio à aprendizagem de conceitos científicos a partir de textos no Ensino Fundamental

Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph

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