Semantic-based topic representation using frequent semantic patterns

Geeganage, Dakshi Tharanga Kapugama; Xu, Yue; Li, Yuefeng

doi:10.1016/j.knosys.2021.106808

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The pre-processed source and suspect documents are a collection of tokenized sentences, and the Vector Space Model (VSM) with term-frequency-inverse sentence frequency (

) weighting reflects the vocabulary of the lemmatized and POS-tagged words contained in these documents [ 8 ]. (

) is a metric developed for use in information retrieval (IR) that attempts to quantify a word’s significance within the context of a phrase [ 28 , 45 , 46 , 47 , 48 ]. The

weight is calculated using:

The number of times a term

appears in any generic sentence

is denoted by term frequency

.…”

Section: The Proposed Qga-based Plagiarism Detection Modelmentioning

confidence: 99%

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A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

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The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.

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“…The pre-processed source and suspect documents are a collection of tokenized sentences, and the Vector Space Model (VSM) with term-frequency-inverse sentence frequency (

) weighting reflects the vocabulary of the lemmatized and POS-tagged words contained in these documents [ 8 ]. (

) is a metric developed for use in information retrieval (IR) that attempts to quantify a word’s significance within the context of a phrase [ 28 , 45 , 46 , 47 , 48 ]. The

weight is calculated using:

The number of times a term

appears in any generic sentence

is denoted by term frequency

.…”

Section: The Proposed Qga-based Plagiarism Detection Modelmentioning

confidence: 99%

“…Sentence weights are assigned to each

by extracting features from

based on

. Both the relevance score and the thematic score may achieve this [ 47 , 48 ].…”

Section: The Proposed Qga-based Plagiarism Detection Modelmentioning

confidence: 99%

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

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Enhanced Topic Representation by Ambiguity Handling

Geeganage

Koggalahewa

et al. 2022

Web Information Systems Engineering – WISE 2022

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An associative knowledge network model for interpretable semantic representation of noun context

Xie

Wang

2022

Complex Intell. Syst.

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Uninterpretability has become the biggest obstacle to the wider application of deep neural network, especially in most human–machine interaction scenes. Inspired by the powerful associative computing ability of human brain neural system, a novel interpretable semantic representation model of noun context, associative knowledge network model, is proposed. The proposed network structure is composed of only pure associative relationships without relation label and is dynamically generated by analysing neighbour relationships between noun words in text, in which incremental updating and reduction reconstruction strategies can be naturally introduced. Furthermore, a novel interpretable method is designed for the practical problem of checking the semantic coherence of noun context. In proposed method, the associative knowledge network learned from the text corpus is first regarded as a background knowledge network, and then the multilevel contextual associative coupling degree features of noun words in given detection document are computed. Finally, contextual coherence detection and the location of those inconsistent noun words can be realized by using an interpretable classification method such as decision tree. Our sufficient experimental results show that above proposed method can obtain excellent performance and completely reach or even partially exceed the performance obtained by the latest deep neural network methods especially in F1 score metric. In addition, the natural interpretability and incremental learning ability of our proposed method should be extremely valuable than deep neural network methods. So, this study provides a very enlightening idea for developing interpretable machine learning methods, especially for the tasks of text semantic representation and writing error detection.

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Semantic-based topic representation using frequent semantic patterns

Cited by 5 publications

References 12 publications

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Enhanced Topic Representation by Ambiguity Handling

An associative knowledge network model for interpretable semantic representation of noun context

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