New Insights into Neural Network Methods for Natural Language Processing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2061
Special Issue Editor
Interests: learning (artificial intelligence); neural nets; electrochemical electrodes; electrochemical sensors; graphite; potassium compounds; voltammetry (chemical analysis); competitive intelligence; computer vision; convolutional neural nets; diseases; electrochemistry; eye; feature extraction; gaze tracking; image enhancement; iron compounds; medical computing; motion estimation; optimization; pattern classification; reduction (chemical); search engines; semantic web; support vector machines
Special Issue Information
Dear Colleagues,
For more than the last two decades, machine learning has attracted dramatic attention in both academia and industry. Popular deep learning concepts have appeared under the machine learning methods umbrella over the years, dominating many areas of artificial intelligence. Natural language is a challenging object for the computing world to handle.
In recent years, with the aid of developing technologies in computation, big data and new algorithms have been used to propose new solutions and targeting natural language processing (NLP), usually making reference to the fact that NLP has evolved from rule-based methods to statistical approaches. This growing research has led to the success of various NLP methods, such as word embeddings and deep learning methods together.
The aim is for the progress of ongoing research and insights into the lessons learned from current advancements in deep learning and NLP application to lead to more advanced research. We invite a wide range of topics and types of work, including evaluations, methodologies, technological innovation, and design in the area of NLP.
Authors will have an opportunity to present their ongoing work, including research, service, prototype, or products addressing one or more of the suggested topics as presented in the general call for participation.
Dr. Kamil Dimililer
Guest Editor
Manuscript Submission Information
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Keywords
- Machine learning
- Deep learning
- Artificial intelligence
- Neural networks
