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ANLP 2020 : The Fourth International Workshop on Advances in Natural Language Processing

Paris, France
Event Date: Dec 14, 2020 - Dec 16, 2020
Submission Deadline: Oct 25, 2020
Notification of Acceptance: Nov 15, 2020
Camera Ready Version Due : Dec 01, 2020

About

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.

Call For Paper

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.

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