Title
Densely Connected Convolutional Neural Networks for Natural Language Procoessing
Faculty Mentor(s)
Bryson Payne
Campus
Dahlonega
Subject Area
Computer Science/GIS
Location
Nesbitt 3203
Start Date
23-3-2018 9:00 AM
End Date
23-3-2018 10:00 AM
Description/Abstract
Densely connected convolutional neural networks are currently one of the best object recognition algorithms. Given the plasticity of neural networks, the DenseNet algorithm should perform similarly in NLP tasks. In its attempt to verify whether the DenseNet algorithm can yield equally impressive results on NLP tasks, this paper has modified the DenseNet algorithm and tested it on text classification. For this purpose, three differently sized datasets have each been encoded as Tf-IDf vectors and word vectors and then the DenseNet’s performance on these different feature sets was compared to more conventional methods including Naïve Bayes classifiers and other neural networks. The paper finds that DenseNets can perform on par with these algorithms but scale especially well with large datasets and semantically rich features.
Densely Connected Convolutional Neural Networks for Natural Language Procoessing
Nesbitt 3203
Densely connected convolutional neural networks are currently one of the best object recognition algorithms. Given the plasticity of neural networks, the DenseNet algorithm should perform similarly in NLP tasks. In its attempt to verify whether the DenseNet algorithm can yield equally impressive results on NLP tasks, this paper has modified the DenseNet algorithm and tested it on text classification. For this purpose, three differently sized datasets have each been encoded as Tf-IDf vectors and word vectors and then the DenseNet’s performance on these different feature sets was compared to more conventional methods including Naïve Bayes classifiers and other neural networks. The paper finds that DenseNets can perform on par with these algorithms but scale especially well with large datasets and semantically rich features.