Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms

English teaching materials serve as a critical vehicle for instruction, with well-designed materials fostering positive learning habits and interests among students. This study employs an ecological philosophy approach and multimodal discourse analysis to examine the modal shifts in college English textbooks. It utilizes the BiFPN network model to capture image features within these materials. Furthermore, the TF-IDF method extracts key terms from the textbook text, while the integration of a CNN-GRU model facilitates the classification of these terms. Additionally, this research introduces relevant computational formulas from text readability theory to evaluate the difficulty levels of these textbooks. The analysis focuses on the “New Vision College English Textbook” series, volumes Compulsory 1 through Compulsory 4. It explores the semantic relationships between text and graphics, chapter-specific reading challenges, and overall text readability indices. Findings indicate that the average proportion of graphic-text equality relations stands at 58.30%, with the highest occurrence of images depicting detailed relationships totaling 217. The Grade Level index for Compulsory 4 reaches 1.61, signifying a high complexity, whereas the Flesch Reading Ease (FRE) score for Compulsory 1 peaks at 75.42, suggesting easier comprehension. In contrast, Compulsory 2 and Compulsory 4 exhibit lower readability scores. Through multimodal discourse analysis, the study delineates the varying difficulty levels across college English textbooks, advocating for a graded approach to textbook development that aligns with students’ evolving competencies. This strategy is poised to significantly boost students’ engagement and facilitate more effective learning.

show abstract

A Study of Output Vocabulary Knowledge in the English Writing Process

Ning

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Vocabulary acquisition is pivotal in enhancing English writing proficiency. Effective integration of output vocabulary into written English is essential for improving students’ compositional skills. This study proposes a methodology for extracting vocabulary from English textual materials and subsequently applying it to student writing endeavors. To ensure the integrity and accuracy of the text materials utilized, this research employs a Long Short-Term Memory (LSTM) algorithm to perform a comprehensive spelling check on the English writing corpus prior to vocabulary extraction. Further, this paper adopts the high-frequency word list and Term Frequency-Inverse Document Frequency (TF-IDF) techniques to identify and evaluate the significance of vocabulary within the texts. Key vocabulary that significantly impacts word importance classification is preliminarily identified using the Graph Convolutional Network-K Nearest Neighbor (GCKN) algorithm. These pivotal words, termed ‘key nodes, ’ form the basis for constructing a network within the English texts. Utilizing the message-passing mechanism, information from associated nodes is aggregated at the central node, facilitating the acquisition of output vocabulary. The study findings indicate that students, after learning and applying the acquired vocabulary, demonstrate considerable improvements in their English writing capabilities. They exhibit a broader and more sophisticated use of vocabulary, leading to marked enhancements in their writing performance and overall English proficiency.

show abstract

Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms

Cited by 11 publications

References 26 publications

Student Grade Prediction and Classification based on Term Frequency-Inverse Document Frequency with Random Forest

Student Grade Prediction and Classification based on Term Frequency-Inverse Document Frequency with Random Forest

Multimodal Discourse Analysis of English Teaching Materials in Colleges and Universities from the Perspective of Ecological Philosophical Perspective

A Study of Output Vocabulary Knowledge in the English Writing Process

Contact Info

Product

Resources

About