Deepak Singh Rana - Academia.edu (original) (raw)
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Papers by Deepak Singh Rana
P,*kt rbool~l b d4tM4• t, t c o Il•. 0 .ntotmatima estmnated t Image Understanding Architecture, ... more P,*kt rbool~l b d4tM4• t, t c o Il•. 0 .ntotmatima estmnated t Image Understanding Architecture, Knowledge-Based Vision, AI Real-Time Computer Vision, Software Simulator, Parallel Processor IL PRICE CODE 17. SECURITY CLASSIFICATION 11. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. UMITATION OF ABSTRACT
An architecture is proposed based on recursive autoencoder for paraphrase detection. The proposed... more An architecture is proposed based on recursive autoencoder for paraphrase detection. The proposed architecture embeds the semantic information by using word representations generated from the neural network language model and the syntactic information by implementing the dependency tree over the recursive autoencoder, where dependency tree reveals the syntactic information of the given sentence in recursive form. The proposed architecture is tested on the MSRP dataset for paraphrase detection and the results are above the baseline. The proposed system reached a moderate accuracy and F1 score for the paraphrase detection test on MSRP dataset.
International Journal of Computer Applications, 2015
International Journal of Computer Applications, 2015
P,*kt rbool~l b d4tM4• t, t c o Il•. 0 .ntotmatima estmnated t Image Understanding Architecture, ... more P,*kt rbool~l b d4tM4• t, t c o Il•. 0 .ntotmatima estmnated t Image Understanding Architecture, Knowledge-Based Vision, AI Real-Time Computer Vision, Software Simulator, Parallel Processor IL PRICE CODE 17. SECURITY CLASSIFICATION 11. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. UMITATION OF ABSTRACT
An architecture is proposed based on recursive autoencoder for paraphrase detection. The proposed... more An architecture is proposed based on recursive autoencoder for paraphrase detection. The proposed architecture embeds the semantic information by using word representations generated from the neural network language model and the syntactic information by implementing the dependency tree over the recursive autoencoder, where dependency tree reveals the syntactic information of the given sentence in recursive form. The proposed architecture is tested on the MSRP dataset for paraphrase detection and the results are above the baseline. The proposed system reached a moderate accuracy and F1 score for the paraphrase detection test on MSRP dataset.
International Journal of Computer Applications, 2015
International Journal of Computer Applications, 2015