Language Processing and Knowledge Extraction

Dear Colleagues,

For some years, Natural Language Processing has followed the trends in artificial intelligence, using algebraic and rule-based approaches. From the simple tasks of tokenization and segmentation, up to the tasks of part-of-speed tagging, or even complex tasks such as machine translations, were highly based in human work on describing formally the task.

In the last years, things have changed. The amount of data on almost every language and every field, together with the computational power evolution, has led to data-oriented approaches, using machine learning algorithms.

Curiously, at first, the goal was not to completely replace human-based rules with a system using only machine learning approaches. As an example, we can consider machine translation. About ten year ago, the main trend was Example Based Machine Translation, that used machine learning to extract portions of texts and their translations (thus, examples of translations). The remaining portion of the translation task was still highly based on translation rules.

More recently, with the boom of the jargon of Deep Learning, these tasks were completely replaced by ML algorithms. Additionally, not just complex tasks, such as machine translation, were affected. Currently, almost any task can be solved using machine learning, given there being are enough data to train a model.

In this Special Issue we are interested in the usage of machine learning approaches on natural language processing, independently of the complexity of the task being solved, and either considering it as a single ML problem or using ML to solve a specific portion. We are especially interested in applications of ML approaches on languages with limited data availability (usually referred as under-resourced languages).

Dr. Alberto Simões
Dr. Pedro Rangel Henriques
Guest Editors

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