Year: 2023
Type: article
Abstract: Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA)a, to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, andRACE. Linguistic features, GloVeword vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.
Source: IEEE Access
Authors: Zhenzhen Li, Han Ding, Shaohong Zhang
Institution: Guangzhou University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,950
Awards: G1527856954, G5866780665
OpenAlex ID: W4386766808