Optimization of Window Size for Calculating Semantic Coherence Within an Essay

Srivastava*, Kshitiz; Dhanda, Namrata; Shrivastava, Anurag

doi:10.14201/adcaij.27184

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Based on this, scholars at home and abroad have conducted in-depth research on it. Srivastava K et al ensured the semantic coherence between different parts of the article by using some sentences of the article to test the semantic coherence [8]. Yang X et al ensured the semantic coherence of the original sentence by using contextaware word replacement [9].…”

Section: Related Workmentioning

confidence: 99%

Construction of Semantic Coherence Diagnosis Model of English Text based on Sentence Semantic Map

Guo

2024

SCPE

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The current English composition automatic correction system rarely involves coherence quality analysis of compositions. Therefore, a semantic coherence diagnosis model for English texts was constructed based on sentence semantic maps, and its effectiveness was verified through experiments. Experimental results show that sub Graphical model 10, 12 and 13 exceed 200 on the first two test texts and 1 on the last two test texts. However, the differences between these three subgraphs on different test texts are not significant, with differences below 30 and 0.3. In addition, when extracting incoherent sentences, the F1 value reaches the optimal value at a threshold of 0.34, which is 87.54%. When the threshold is fixed at 0.34, the accuracy of extracting non coherent sentences also increases with the number of articles, reaching a maximum of 88.43%. At the same time, there was no significant difference in accuracy, recall, and F1 values among different English composition numbers, maintaining between 83% and 89%. The Pearson coefficient calculated in the actual comparison with the teacher's manual composition score is 0.6025, indicating a strong correlation between the two, indicating that the diagnostic results are reliable. Overall, the diagnostic model constructed in the study has strong accuracy and effectiveness, and is practical in the diagnosis of semantic coherence in actual English texts.

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Section: Related Workmentioning

confidence: 99%

Construction of Semantic Coherence Diagnosis Model of English Text based on Sentence Semantic Map

Guo

2024

SCPE

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“…The employed attributes quantify the spatial central tendency and spatial dispersion. These attributes are defined based on the points in the semantic space [49,51,52] It is employed in the determination of the total range of variation in a given point distribution.…”

Section: Step 4: Spatial Data Analysismentioning

confidence: 99%

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

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Optimization of Window Size for Calculating Semantic Coherence Within an Essay

Cited by 2 publications

References 22 publications

Construction of Semantic Coherence Diagnosis Model of English Text based on Sentence Semantic Map

Construction of Semantic Coherence Diagnosis Model of English Text based on Sentence Semantic Map

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

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