The aim of this international journal is to provide a multi-disciplinary platform to report on original and significant research in the fields of Natural Language Processing and Understanding, Computational Linguistics, Language technologies, and applications.
The prime focus of this journal is to provide platform for research in languages that has limited exposure, thus submissions on non-Latin languages, e.g. Arabic, Chinese, Japanese, and Indian languages, is particularly encoruaged. New techniques and nontraditional approaches are particularly welcomed.
The IJCLR focuses on all areas of Computational Linguistics research and its applications. Full and short research papers are welcomed. Themes and areas of interest include, but not limited to:
1. - Natural Language Processing
1.1 Corpus development
1.2 Tagging Systems
1.3 Stemming
1.4 Parsing and syntactical analysis
1.5 Dictionaries and corpus-based language engineering
1.6 Grammar
2- Natural Language Understanding
1.1 Text analysis
1.2 Ontology
1.3 Formal semantics
1.4 Subliminal contents, e.g. emotions
3- Language-based Knowledge Engineering
4.1 Text and data mining
4.2 Ontologies
4.3 Knowledge acquisition
4.4 Natural language based programming languages
4.5 Knowledge representation and reasoning
4- Cognitive models and AI techniques
4.1 Graph based models, semantic nets, neural networks, and cognitive maps
4.2 Evolutionary Computation
4.3 Psychology of Language
4.4 Auditory Cognition
5- Language Generation
5.1 Dialogue-based systems
5.2 Creative and writing systems
5.3 language synthesis
5.4 Translation
5.5 Computational critics
6- Multi-modalities Computational Linguistics
6.1 Speech recognition, speech-text conversions
6.2 Speech analysis and textual tagging
6.3 Speech production and subliminal content, e.g. emotions recognition
7- Applications and Systems
7.1 Search and information retrieval
7.2 Web applications
7.3 Forensics
7.4 Cognitive systems, e.g. robotics
7.5 Question-answer systems
7.6 Methodologies
7.7 Multi-liinguage and translation systems
7.8 Documents classifiers
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