2021/11/02 by Shaw‐Hwa Lo, Shaw-Hwa Lo, Lo, Shaw-Hwa +2
Computer Science · #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Topic Modeling #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2112.02997
arxiv created 2021/11/02 · openalex publication_date 2021/11/02 · arxiv updated 2021/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models is capable making good predictions yet there is lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the "dagger technique". First, the paper proposes a novel influence score (I-score) to detect and search for the important language semantics in text document that are useful for making good prediction in text classification tasks. Next, a greedy search algorithm called the Backward Dropping Algorithm is proposed to handle long-term dependencies in the dataset. Moreover, the paper proposes a novel engineering technique called the "dagger technique" that fully preserve the relationship between explanatory variable and response variable. The proposed techniques can be further generalized into any feed-forward Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and any neural network. A real-world application on the Internet Movie Database (IMDB) is used and the proposed methods are applied to improve prediction performance with an 81% error reduction comparing with other popular peers if I-score and "dagger technique" are not implemented.