Computational Linguistics and Artificial Intelligence

Dear Colleagues,

The field of Computational Linguistics (CL), in its engineering domain of natural language processing (NLP), has an important role to play in Artificial Intelligence (AI). AI, in turn, refers to what information about the human language structure is being transmitted to the machine. Built on the foundations of this relationship—from small prototypes and theoretical models to robust processing and learning systems applied to large corpora—is the exploration—alternative or joint—of logical relationships that generate meaning as well as of context-sensitive relationships. This is the key to the learning principles that make the interaction between CL and AI no longer nebulous and can endow computers with intelligence. The human linguistic process is simultaneously based on axiomatic (contextual) and logical resources. This is the universal structure of language, capable of elucidating aspects of the fundamental limitations of learning algorithms and helping technicians to design algorithms that circumvent these limitations. The ease that intelligent systems have in assimilating logical tasks, and their difficulty in learning to represent the context, show that a Computational Linguistics that overcomes architectural limitations needs to manage this double linguistic structure, as a process that is both axiomatic (contextual) and logical. This perspective makes it possible—for example, when determining the meaning of sentences—to transmit to the machine methods used in CL that cover logical and contextual aspects. This ability meets the definition of Artificial Intelligence, making systems more powerful and improving their efficiency in corpora annotation, evaluation of NLP systems, procedures in computational linguistics and NLP, engineering tasks to which these procedures are applied, and approaches to machine learning, among others. There is, not by chance, an overlap of domains that stems from this common structure and justifies the permeability between computer science, linguistics, psychology, philosophy, and mathematics, among other branches of science. This wide and dynamic spectrum of scientific fields makes this Special Issue a reference containing important results for various classes and tasks of the linguistic process—at the intersection between human and machine. Thus, it is expected that graduate students and researchers in their respective areas will address solutions to overcome the complexity of the language transmitted to Artificial Intelligence systems and to make the latter more intuitive.

Dr. Dioneia Motta Monte-Serrat
Prof. Dr. Francesco M. Donini
Guest Editors

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