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IJNLC 2021 : International Journal on Natural Language Computing

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Link: http://airccse.org/journal/ijnlc/index.html
 
When N/A
Where N/A
Submission Deadline Jun 19, 2021
Notification Due Jul 19, 2021
Final Version Due Jul 27, 2021
Categories    natural language processing   NLP   ontology   text mining
 

Call For Papers

International Journal on Natural Language Computing (IJNLC)

Citations, h-index, i10-index of IJNLC

Scope and Topics

Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.

Topics of interest include but are not limited to, the following

  • Phonology, Morphology
  • Chunking/Shallow Parsing
  • Parsing/Grammatical Formalisms
  • Semantic Processing
  • Lexical Semantics
  • Ontology
  • Linguistic Resources
  • Statistical and Knowledge based methods
  • POS tagging
  • Discourse
  • Paraphrasing/Entailment/Generation
  • Machine Translation
  • Information Retrieval
  • Text Mining
  • Information Extraction
  • Question Answering
  • Dialog Systems
  • Spoken Language Processing
  • Speech Recognition and Synthesis

Paper Submission

Authors are invited to submit papers for this journal through E-mail  ijnlcjournal@airccse.org or through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

 

Submission Deadline:June 19, 2021
Authors Notification:July 19, 2021
Final Manuscript Due:July 27, 2021
Publication Date:Determined by the Editor-in-Chief

Current Issue


April 2021, Volume 10, Number 2

An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques   Full Text
Chidinma A. Nwafor and Ikechukwu E. Onyenwe, Nnamdi Azikiwe University Awka, Nigeria

Integrating Extracted Information from Bert and Multiple Embedding Methods with the Deep Neural Network for Humour Detection   Full Text
Rida Miraj and Masaki Aono, Toyohashi University of Technology, Japan

February 2021, Volume 10, Number 1

Applying the Affective Aware Pseudo Association Method to Enhance the Top-N Recommendations Distribution to Users in Group Emotion Recommender Systems   Full Text
John Kalung Leung, Igor Griva and William G. Kennedy, George Mason University, USA

Editor In Chief


Editorial Board Members

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