Special Issue on
Current Approaches and Applications in Natural Language Processing

Applied Sciences. MDPI Open Access Journal
JCR Impact Factor: 2.474 (Q2 in Engineering, multidisciplinary, 2019)

Deadline for manuscript submissions: 20 September 2021.

Special issue information


Dear colleague,

We're glad to invite you to submit a paper to the special issue on "Current
Approaches and Applications in Natural Language Processing", for the open
access journal Applied Sciences.

Current approaches in Natural Language Processing (NLP) have shown
impressive improvements in many major tasks: machine translation, language
modelling, text generation, sentiment/emotion analysis, natural language
understanding, question answering, among others. The advent of new methods
and techniques like graph-based approaches, reinforcement learning or deep
learning have boosted many of the tasks in NLP to reach human-level (and
even further) performance. This has attracted the interest of many
companies, so new products and solutions can profit from the advances of
this relevant area within the artificial intelligence domain.

This Special Issue focuses on emerging techniques and trendy applications of
NLP methods is an opportunity to report on all these achievements,
establishing a useful reference for industry and researchers on cutting edge
human language technologies. Given the focus of the journal, we expect to
receive works that propose new NLP algorithms and applications of current
and novel NLP tasks. Also, updated overviews on the given topics will be
considered, identifying trends, potential future research areas and new
commercial products.


Topics of interest
------------------------------------
The topics of this Special Issue include but are not limited to:
- Question answering: open-domain Q&A, knowledge-based Q&A...
- Knowledge extraction: Relation extraction, fine-grained entity
recognition...
- Text generation: summarization, style transfer, dial...
- Text classification: Sentiment/emotion analysis, semi-supervised and
zero-shot learning...
- Behaviour modelling: early risk detection, cyberbullying, customer
modelling...
- Dialogue systems: chatbots, voice assistants...
- Reinforcement learning
- Data augmentation
- Graph based approaches
- Adversarial approaches
- Multi-modal approaches
- Multi-lingual/cross-lingual approaches

More information at
https://www.mdpi.com/journal/applsci/special_issues/Language_Processing


Co-editors
-----------------------
Prof. Dr. Arturo Montejo-Ráez
Guest Editor
SINAI Research Group, CEATIC, Universidad de Jaén, 23071 Jaén, Spain

Dr. Salud María Jiménez-Zafra
Guest Editor
SINAI Research Group, CEATIC, Universidad de Jaén, 23071 Jaén, Spain