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Article

Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network

Department of Statistics, Columbia University, New York, NY 10027, USA
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Academic Editor: Andreas Holzinger
Mach. Learn. Knowl. Extr. 2021, 3(4), 922-945; https://doi.org/10.3390/make3040046
Received: 21 October 2021 / Revised: 16 November 2021 / Accepted: 17 November 2021 / Published: 19 November 2021
(This article belongs to the Section Learning)
Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a 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 to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for making good predictions 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 preserves the relationship between the explanatory variable and the 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 compared to other popular peers if I-score and “dagger technique” are not implemented. View Full-Text
Keywords: neural networks; interaction-based learning; I-score; dagger technique neural networks; interaction-based learning; I-score; dagger technique
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MDPI and ACS Style

Lo, S.-H.; Yin, Y. Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network. Mach. Learn. Knowl. Extr. 2021, 3, 922-945. https://doi.org/10.3390/make3040046

AMA Style

Lo S-H, Yin Y. Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network. Machine Learning and Knowledge Extraction. 2021; 3(4):922-945. https://doi.org/10.3390/make3040046

Chicago/Turabian Style

Lo, Shaw-Hwa, and Yiqiao Yin. 2021. "Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network" Machine Learning and Knowledge Extraction 3, no. 4: 922-945. https://doi.org/10.3390/make3040046

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