New Machine Learning and Deep Learning Techniques in Natural Language Processing

Dear Colleagues,

Natural language processing (NLP) represents a domain where machine learning has been highly applied in recent decades. NLP stopped representing a domain of interest exclusively for enthusiasts a long time ago: nowadays, as important global companies are interested in analyzing the sentiments from product or movie reviews, in automatically obtaining the opinions or themes of the discussions from users from social networks, it is crucial to detect fake news, and, as well as this, there are surely many brokers interested in predicting stock market trends by extracting the sentiments from financial news articles. Furthermore, there are definitely far more examples of NLP applications of high interest for the wider population.

Artificial intelligence has recently obtained an important boost with the rise of deep learning, and this fresh wave surely has had an impact on the applications of NLP. The new techniques based on deep learning models have surpassed, in most cases, traditional machine learning frameworks, although there are conventional approaches or new computational intelligence algorithms that still lead to state-of-the-art results in NLP applications.

The purpose of this Special Issue is to collect articles where the old and new challenges in NLP are treated by new approaches, either using traditional machine learning techniques or via deep learning approaches. Hybrid techniques, especially between machine learning and metaheuristics, towards improving the accuracy represent another type of framework that would be well-suited in the collection of papers gathered in this Special Issue. Moreover, the involvement of computational intelligence methods and algorithms, e.g., evolutionary algorithms (particularly genetic algorithms), swarm intelligence, as well as other nature and non-nature inspired metaheuristics-based approaches, for enhancing the results would represent another type of successful applications that would fit well within the papers of the Special Issue. As regards the type of NLP applications, we encourage submissions dealing with text classification, sentiment analysis, authorship attribution, text document clustering, detection of fake news, machine translation, text summarization, development of chatbots, grammar checking, and voice assistants, but we do not limit the examples to these.

We look forward to receiving your contributions.

Dr. Nebojsa Bacanin
Dr. Catalin Stoean
Guest Editors

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