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Publication Type : Conference Paper
Publisher : 2020 6th IEEE Congress on Information Science and Technology
Source : 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2020, pp. 372–377.(The Best Paper Award Winner) (2020)
Keywords : Compositional Semantics, Natural Language Inference, Recursive Neural Network, Sentence modeling
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : Recursive neural networks (Tree-RNNs) based on dependency trees are ubiquitous in modeling sentence meanings as they effectively capture semantic relationships between non-neighborhood words. However, recognizing semantically dissimilar sentences with the same words and syntax is still a challenge to Tree-RNNs. This work proposes an improvement to Dependency Tree-RNN (DT-RNN) using the grammatical relationship type identified in the dependency parse. Our experiments on semantic relatedness scoring (SRS) and recognizing textual entailment (RTE) in sentence pairs using SICK (Sentence Involving Compositional Knowledge) dataset show encouraging results. The model achieved a 2% improvement in classification accuracy for the RTE task over the DT-RNN model. The results show that Pearson's and Spearman's correlation measures between the model's predicted similarity scores and human ratings are higher than those of standard DT-RNNs.
Cite this Research Publication : Jeena Kleenankandy and Abdul Nazeer K. A., “Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed Dependencies”, in 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2020, pp. 372–377.(The Best Paper Award Winner), 2020.