Advances in Machine Learning Methods for Natural Language Processing and Computational Linguistics

Dear Colleagues,

Machine learning (ML) algorithms can be used to analyze vast volumes of information, identify patterns and generate models capable of recognizing them in new data instances. This allows us to address complex tasks with the only constraint being the necessity of a suitable training database.

Furthermore, today's digital society provides access to a vast range of raw data, but also generates the need for managing them effectively. This makes up natural language processing (NLP), a collective term referring to the automatic computational treatment of human languages for which purely symbolic techniques show clear limitations, a popular field for exploiting ML capacities. The same is true for computational linguistics (CL), which is more concerned with the study of linguistics.

However, this collaborative framework must be based on a formally well-informed strategy to ensure its reliability. In this context, this Special Issue focuses on both the application of ML techniques to solve NLP and CL tasks and on the generation of linguistic resources to enable this, for example, the construction of syntactic structures without recurse to tree banks for training, which would greatly simplify the implementation of statistical-based parsers, especially when dealing with out-of-domain scenarios or low-resource languages. By way of a more applicative issue, we could address the generation of models allowing efficient contextual representations, a nontrivial task when dealing with large-scale or multiple documents, but essential for language understanding.

Prof. Dr. Manuel Vilares-Ferro
Prof. Dr. Pavel Brazdil
Prof. Dr. Gaël Dias
Guest Editors

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