N/A Modern Approaches in Natural Language Processing

Natural Language Processing (NLP) is one of the major branches in the emerging field of Artificial Intelligence (AI). Classical approaches in this area were mostly based on parsing and information extraction techniques, which suffered from great difficulty when dealing with very large textual datasets available in practical applications. This issue can potentially be addressed with the recent advancement of the Deep Learning (DL) techniques, which are naturally assuming very large datasets for training. In fact, NLP research has witnessed a remarkable achievement with the introduction of Word Embedding techniques, which allows a document to be represented meaningfully as a matrix, on which major DL models like CNN or RNN can be deployed effectively to accomplish common NLP tasks. Gradually, NLP scholars keep developing specific models for their areas, notably attention-enhanced BiLSTM, Transformer and BERT. The births of those models have introduced a new wave of modern approaches which frequently report new breaking results and open much novel research directions. The aim of this paper is to give readers a roadmap of those modern approaches in NLP, including their ideas, theories and applications. This would hopefully offer a solid background for further research in this area.