Are Deep Learning Approaches Suitable for Natural Language Processing?

• Published in: Natural Language Processing and Information Systems pp. 343 - 349
• Main Authors: Alshahrani, S., Kapetanios, E.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial neural networks; Convolutional neural networks; Deep learning; Long short-term memory; Natural language processing; Recursive neural networks
• ISBN: 9783319417530; 3319417533
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-41754-7_33

In recent years, Deep Learning (DL) techniques have gained much attention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understanding, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).