A Study of Deep Learning in Text Analytics

Abstract

Most of the data present today on Web is in the form of text. This text data is a rich source of information, but it is difficult to process because it is unstructured in nature. Various techniques have been developed in past to process this text data for information retrieval and text mining. Many machine learning techniques have been developed in past to drive valuable information from text data. However, these methods require a lot of preprocessing and feature extraction prior to processing which increases manual task. There are a lot of human interactions which not only consumes time but also makes the whole system where rigid and hard to generalize. Deep learning architectures mitigate these limitations. They require less human intervention and create better solutions than other machine learning techniques. Deep architectures are neural networks with multiple processing layers of neuron with each layer having a specific task. Various deep learning architectures have been developed till date with each having a specific task. These architectures have contributed significantly in the domain of text processing. Word2Vec and GloVe have helped to represent text in vector forms. Vector representation helps to process text easily. Architectures like CNN & autoencoders have helped to automate the task of feature extraction. On the other hand, architectures like LSTM and RNN are used for text processing. Text classification, document summarization, question answering, machine translation, caption generation, and speech recognition are some of the areas where improvements have been found due to deep learning architectures. This paper first introduces the text preprocessing techniques and feature extraction methods. Later on, a study of contributions of deep learning architectures in text processing is presented.