The workshop aims at demonstrating recent Advances in Natural Language Processing (ANLP) by bringing together leading academicians, scientists, researchers and practitioners to discuss their findings in NLP. This area of research has been constantly gaining momentum and attention over the past few years as textual material become available in abundance (social networks and web content) and advanced computational power become accessible. This resulted in an interesting array of practical applications. This is evident in the number of conferences, conference tracks, workshops, special issues of journals dedicated to or issues related to ANLP. Another indication of this growing interest is the surge of papers, books, and other forms of publications related to NLP. Noticeably, deep learning is dominating NLP as it did to other areas of artificial intelligence. Deep learning is the modern application of earlier neural network technology but with multiple layers that made possible with modern computing technology. The application of deep learning technology to NLP is progressing at a fast pace, and it is anticipated that we will see many exciting new discoveries here in the next few years. Therefore, the objective of this workshop is to present current and future advances in NLP. Authors are encouraged to submit their original work, which is not submitted elsewhere, to this workshop.

Proceedings of the workshops will be published by the IEEE Conference Publishing Services (CPS) and will be submitted for inclusion in the IEEE-Xplore and the IEEE Computer Society (CSDL) digital libraries. Authors are encouraged to submit their original work, which is not submitted elsewhere, to this workshop. The topics of the workshop include but not limited to:

* Morphological analysis.
* Stemming, Tokenization, segmentation, chunking, parsing, POS.
* Information retrieval and extraction.
* Named Entity Recognition and Keyword extraction.
* Word embedding models.
* Text summarization and compression.
* Event extraction and semantic role labeling.
* Sentiment analysis and classification.
* Dialectal modeling.
* Machine translation.
* Deep learning applications.
* Speech recognition, synthesis, and speaker identification.
* NLP (annotated) resources such as dictionaries, thesauri, lexicons, ontologies, datasets, etc.
* Applications and technologies of NLP.