Course Description
Fundamental concepts of Natural Language Processing (NLP) and deep learning methods are introduced. Word level syntactic and semantic processing from linguistic and algorithmic perspectives are discussed. Statistical acquisition and modern quantitative techniques are presented. Deep learning neural network paradigms are introduced. Deep Learning techniques in NLP utilizing different learning methods and architectures such as Multi-layer perceptron, convolutional, encoder-decoder, Greedy-Wiser DBN will be used in solving underlying problems. Comparisons with classical methods (HMM/statistical) are presented. Corpuses preparation is presented. Research or application-oriented project in text and/or speech recognition will be conducted.