Titlebook: Natural Language Processing and Information Systems; 24th International C Elisabeth Métais,Farid Meziane,Mohamad Saraee Conference proceedi

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Deceptive Reviews Detection Using Deep Learning Techniquesle lengths of these reviews. We have proposed two different methods – Multi-Instance Learning and Hierarchical architecture to handle the variable length review texts. Experimental results on multiple benchmark datasets of deceptive reviews have outperformed existing state-of-the-art. We evaluated t

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Structure-Based Supervised Term Weighting and Regularization for Text Classificationn into the learning process. A graph is built for each class from the pre-classified training documents and structural information in the graphs is used to calculate the supervised term weights and to define the groups for structured regularization. Experimental results for six text classification t

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Gated Convolutional Neural Networks for Domain Adaptationtional Neural Networks give significantly better performance on target domains than regular convolution and recurrent based architectures. While complex architectures like attention, filter domain specific knowledge as well, their complexity order is remarkably high as compared to gated architecture

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Intent Based Association Modeling for E-commerceased on the intent patterns, we look at generating association rules that model purchasing behavior. Our studies show that users typically go through multiple states of intent behavior, dependent on key features of products under consideration. We test the behavioral model by coupling it with Google

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