QuIET: A Text Classification Technique Using Automatically Generated Span Queries

Polychronopoulos, Vassilis; Pendar, Nick; Jeffery, Shawn R.

doi:10.1109/icsc.2014.18

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…In 2009 there was, a big number of difference between two types of publication as related statistical data shows that conference article publication was five times higher than journal article publication. [26], [31], [27], [32]- [35]. (c) Insufficiency of resources: This is closely related to limitation of a specific area than to be referred to as a problem.…”

Section: Literature Search Resultsmentioning

confidence: 99%

“…Examples are in [10], [28]-[30]. (b) Malfunction of existing techniques: This problem concerns weaknesses or loopholes of existing approaches.Researchers point out gaps of current techniques which do not work well in a specific area and try to solve that problem by proposing new methods such as in[26],[31],[27],[32]-[35]. (c) Insufficiency of resources: This is closely related to limitation of a specific area than to be referred to as a problem.…”

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confidence: 99%

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Trends and Patterns of Text Classification Techniques: A Systematic Mapping Study

Maw

Balakrishnan

Rana

et al. 2020

MJCS

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Due to the mass availability of textual data on Web, text classification (TC), classifying texts into predetermined sets becomes a spotlight for researchers. A number of TC applications have been proposed yet very few studies reported an overview of TC research area in a proper and systematic manner. This paper aims to provide an overview of TC research trends and gaps by structuring and analyzing research patterns, encountered problems and problem-solving methods in TC. In other words, this study highlights problem types, data sources, choice of language of text and types of applied techniques in TC. An intensive systematic study is conducted by applying guidelines proposed by Petersen and colleagues in 2007. In this paper, ninety-six literatures from five electronic databases from 2006 to 2017 were systematically reviewed and followed each and every step properly in accordance with systematic mapping study. Nine main problems in TC research area were identified and significant findings which highlighted the evolution of TC research within the past 12 years were investigated. Different from other review articles, this paper highlighted issues and technical gaps of TC area in a useful and effective manner.

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Section: Literature Search Resultsmentioning

confidence: 99%

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confidence: 99%

Trends and Patterns of Text Classification Techniques: A Systematic Mapping Study

Maw

Balakrishnan

Rana

et al. 2020

MJCS

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“…This system has extended in the paper [11] and they found that a small set of disjunctive Lucene SpanFirst queries meet both accuracy and classifier readability effectively. QuIET in [26] is also automatically generates a set of span queries from a set of annotated documents and uses the query set to categorize unlabeled texts but is not a GA based algorithm.…”

Section: Rule-based Direct Methodsmentioning

confidence: 99%

Human Friendliness of Classifiers: A Review

Haddela

Hirsch

Gaudoin

et al. 2021

Advances in Intelligent Systems and Computing

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“…Equation ( 2), that is, recall, is the fraction of the number of true positive documents that the classifier returns from the number of actual documents of the category in the corpus (Grandini et al, 2020;Polychronopoulos et al, 2014).…”

Section: Stage Iii: Model Evaluationmentioning

confidence: 99%

An analysis of search query evolution in document classification and clustering

Haddela Kankanamalage

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With the increasing use of data analytics in decision-making processes today, the analysis of document collections for various purposes has become a widely accepted area of research. Document classification and clustering are two intensely investigated and active areas of research due to the complex nature of the problem and its impact on society. However, many of the popular methods developed to classify and cluster documents with high accuracy lack explanation to end users, which affects the trustworthiness of certain applications among them. Therefore, it is crucial to improve explainable classification and clustering methods. One approach that has shown promise in this regard is the evolved search query (eSQ), a genetic algorithm (GA)-based approach for classification and clustering. GA-based methods excel at finding highly optimized solutions for complex problems, and eSQ has utilized this capability to develop classification and clustering methods that are also human interpretable. The primary focus of this study is to analyse the eSQ approach to document classification and clustering with an emphasis on explainability. The investigation covers three perspectives of the eSQ-based methods: explainability, document classification, and document clustering. This thesis presents a taxonomy for classification based on human friendliness, empirical observations on the performance of eSQ classifiers using different feature selection methods, the effectiveness of eSQ classifiers for Sinhala documents, and the performance of eSQ clustering for Sinhala documents. The research contributes significantly by categorizing popular classification methods using the new taxonomy, integrating feature selection methods into eSQ classifiers, enhancing Apache Lucene by incorporating the Sinhala language with basic pre-processing tools, and improving eSQ hybrid single word clustering methods. Notably, the eSQ-based classification and clustering methods demonstrate superior performance when document categories overlap.

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QuIET: A Text Classification Technique Using Automatically Generated Span Queries

Cited by 3 publications

References 15 publications

Trends and Patterns of Text Classification Techniques: A Systematic Mapping Study

Trends and Patterns of Text Classification Techniques: A Systematic Mapping Study

Human Friendliness of Classifiers: A Review

An analysis of search query evolution in document classification and clustering

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