SLIE 2021 - Semantic, Logics, Information Extraction and AI

Today, people working in Natural Language Processing (NLP) domain, especially Semantic,
Logics and Information Extraction (SLIE), are often members of interdisciplinary teams,
including computer scientists, experts in artificial intelligence, mathematicians, statisticians,
logicians, linguists, philosophers, cognitive scientists, cognitive psychologists, psycholinguists,
anthropologists and neuroscientists, among others. NLP-SLIE is not just a subfield of computer
science, information engineering, and artificial intelligence. It is, also, connected to cognitive
sciences through the development of cognitive theories. We can see NLP-SLIE divided into
two parts: (1) its theoretical descriptions; (2) its concrete implementations. The theoretical
aspect includes philosophical hypothesis, psychological hypothesis, linguistics methods as
semantics, syntax, morphology, etc., or numerical methods as classification, neural networks,
Naive Bayes, deep learning, etc. At the present stage of research, there still a gap between
theoretical approaches and computer implementations. There are many computer applications
without any solid theoretical foundations, and there are many theoretical methods with no
computer implementation. Theoretical Methods and computational implementations for the
NLP-SLIE must take into account this epistemological chain for to lead to effective, robust and
reliable systems.
Papers and contributions are encouraged for any work relating to Natural Language Processing.
Topics of interest may include (but are in no way limited to):
• Philosophy of language – new developments,
• Cognitive semantics,
• Logics of language,
• Language modeling,
• Computational linguistics (lexicology; morphology; syntax; semantics),
• Information extraction,
• Domain ontologies, linguistic ontologies,
• Knowledge processing,
• Translation,
• Text-mining.
• Deep-Learning.
• etc.