Work

Informationization Design of College English Corpus Using Deep Learning Algorithms

Year: 2026

Type: article

Abstract: This study proposes the ISSO-SNN model for semantic readability assessment in a college English corpus, addressing limitations of traditional methods. Using NLP preprocessing and TF-IDF features, the model leverages a Siamese network optimized with ISSO for improved prediction. The study proposes an Intelligent Shuffled Shepherd Optimized Siamese Neural Network (ISSO-SNN) model for semantic readability assessment in a college English corpus. Implemented in Python 3.11, it achieved high performance (accuracy 0.98, precision 0.97, recall 0.97 , F1 0.98 ), supporting effective and personalized English learning.

Source: DOAJ (DOAJ: Directory of Open Access Journals)

DOI: 10.6180/jase.202609_32.023

Authors: Cheng Lin, Ruixue Li

Institution: Jiujiang University

Language: English

Topic: Text Readability and Simplification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W7160707223

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