Leveraging the meta-embedding for text classification in a resource-constrained language

Abstract

This paper proposes an intelligent text classification framework for a resource-constrained language like Bengali, which is considered a challenging task due to the lack of standard corpora, appropriate hyper-parameter tuning method, and pre-trained language-specific embedding. The proposed framework comprises an average meta-embedding feature fusion module and a convolutions neural network module called AVG-M+CNN. This work also proposes an algorithm, i.e., automatic hyperparameter tuning and selection, for enhancing the performance of the AVG-M+CNN technique. All meta-embedding models are evaluated using the intrinsic, e.g., semantic, syntactic, relatedness word similarity, analogy tasks and extrinsic evaluators. The intrinsic evaluator evaluates 200 Bengali semantic, syntactic and relatedness word pairs. Spearman (ρˆ), Pearson (rˆ) and cosine similarity correlations are used to evaluate 18 individual embedding and 9 meta-embedding models. The 3COSADD and 3COSMUL evaluators evaluate the 300 analogy tasks. The extrinsic evaluator evaluates a total of 156 classification models on four corpora: BARD, IndicNLP, Prothom-Alo and BTCC11 (a newly developed corpus having eleven distinct categories). Among these, the AVG-M+CNN model achieves the highest accuracy regarding four Bengali corpora: 95.92±.001% for BARD, 93.10±.001% for Prothom-Alo, 90.07±.001% for BTCC11 and 87.44±.001% for IndicNLP, respectively.