DWSA: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining

Yue, Tan; Li, Yong; Hu, Zonghai

doi:10.3390/electronics10192443

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…For the semantic similarity measuring task, constant weighting assumption-based semantic similarity [45] measure between two concepts/words holds better performance for the semantic representation of the concept/words but holds the same weighting relevance. Later, it found that the weight propagation mechanism [46,47] for augmenting input with semantic information achieves desired performance and removes the same weighting curse for concepts/words. Recently, Liu J. et el.…”

Section: Literature Reviewmentioning

confidence: 99%

Corpus Statistics Empowered Document Classification

et al. 2022

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In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of the documents. Gaussian mixture-based clustering is widespread for capturing rich thematic semantics but ignores emphasizing potential terms in the corpus. Moreover, the soft clustering approach causes long-tail noise by putting every word into every cluster, which affects the natural thematic representation of documents and their proper classification. It is more challenging to capture semantic insights when dealing with short-length documents where word co-occurrence information is limited. In this context, for long texts, we proposed Weighted Sparse Document Vector (WSDV), which performs clustering on the weighted data that emphasizes vital terms and moderates the soft clustering by removing outliers from the converged clusters. Besides the removal of outliers, WSDV utilizes corpus statistics in different steps for the vectorial representation of the document. For short texts, we proposed Weighted Compact Document Vector (WCDV), which captures better semantic insights in building document vectors by emphasizing potential terms and capturing uncertainty information while measuring the affinity between distributions of words. Using available corpus statistics, WCDV sufficiently handles the data sparsity of short texts without depending on external knowledge sources. To evaluate the proposed models, we performed a multiclass document classification using standard performance measures (precision, recall, f1-score, and accuracy) on three long- and two short-text benchmark datasets that outperform some state-of-the-art models. The experimental results demonstrate that in the long-text classification, WSDV reached 97.83% accuracy on the AgNews dataset, 86.05% accuracy on the 20Newsgroup dataset, and 98.67% accuracy on the R8 dataset. In the short-text classification, WCDV reached 72.7% accuracy on the SearchSnippets dataset and 89.4% accuracy on the Twitter dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Corpus Statistics Empowered Document Classification

et al. 2022

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“…The traditional teaching approaches, such as lectures and group discussions, were blamed for the student's poor academic performance in technical drawing. Interactive teaching techniques are one of the tried-and-true approaches to prevent children from performing poorly in school and to increase their enthusiasm for learning [18], [19]. In order to determine how an intelligent tutoring system based on the Web influenced student achievement and interest in technical drawing in Niger State, Nigeria.…”

Section: Introductionmentioning

confidence: 99%

Web-Based Intelligent Tutoring System On Students Achievement And Interest In Technical Drawing In Niger State

Hassan

Usman²,

Bala³

et al. 2023

JISTR

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The study examines how a web-based innovative coaching system affects students' performance and interest in technical drawing in Niger, Nigeria. The hypotheses created in answer to the two study questions are evaluated using the A.05 significance threshold. The investigation was conducted utilizing a pre-post non-equivalent control group quasi-experimental study design. The study's participants comprised 428 National Technical College (NTC) II, students. The sample size for this study was 180 students, of whom 68 were in the experimental group, and 112 were in the control group. The experimental group employed Simple Random Sampling (SRS) and intentional sampling techniques. The instruments used to gather data are the Technical Drawing Achievement Test (TDAT) and the Technical Drawing Interest Inventory (TDII). The reliability coefficient for TDAT is 0.77, whereas the reliability coefficient for TDII is 0.79 using the Pearson Product Moment Correlation coefficient. The mean was used to answer research questions, and ANCOVA was used to evaluate the hypotheses. The study's findings demonstrated that web-based intelligent tutoring solutions are more successful in raising student achievement and piquing their interest in technical drawing than traditional teaching techniques. It was suggested that professors, particularly those teaching technical drawing, employ a web-based intelligent tutoring system for technical college students to enhance students' academic performance and pique their interest in technical drawing.

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