A novel image clustering method based on coupled convolutional and graph convolutional network

Li, Rangjun

doi:10.4108/eai.16-11-2021.172132

Cited by 1 publication

(1 citation statement)

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“…Jiao Yin et al [15] investigated knowledge transfer between subgraphs extracted from the same knowledge graph, leveraging the graph structure to extract data relationships and applying them in software vulnerability detection. Rangjun Li [16] suggested that considering both intrinsic attribute information and inter-sample structural information simultaneously enhances the feature recognition capability of the model. Guinaudeau and Strube et al [5] extended the entity grid into an entity graph model to represent a text in a graph, thus analysing the coherence of the text in its entirety.…”

Section: Related Workmentioning

confidence: 99%

Semantic Coherence Analysis of English Texts Based on Sentence Semantic Graphs

Deng,

Wang,

Huang

et al. 2023

ICST Transactions on Scalable Information Systems

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With the reform of China's education industry, more and more universities are using computers to conduct examinations. For the automatic correction of essays as subjective questions, existing automatic English text scoring systems suffer from insufficient extraction of coherence information and low accuracy when analysing text coherence. Therefore, this paper proposes an unsupervised semantic coherence analysis model for English texts based on sentence semantic graphs, taking Chinese students' English compositions as the research context. Guided by the semantic coherence theory, the English text is represented as a sentence semantic graph, and an improved VF2 subgraph matching algorithm is used to mine the frequently occurring subgraph patterns in the sentence semantic graph. After that, the set of frequent subgraphs is generated by filtering the subgraph patterns according to their frequencies, and the subgraph frequency of each frequent subgraph is calculated separately. Finally, the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graphs are extracted to quantify the overall coherence quality of English texts. The experimental results show that the model proposed in this paper has higher accuracy and practical value compared with the current methods of coherence analysis.

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Section: Related Workmentioning

confidence: 99%