Processamento de linguagem natural em Portugu\^es e aprendizagem profunda para o dom\'inio de \'Oleo e G\'as

TL;DR

This paper reviews deep learning NLP techniques and their applications in the Oil and Gas domain, focusing on Portuguese language challenges and the scarcity of domain-specific corpora for effective information processing.

Contribution

It provides a comprehensive review of NLP deep learning methods applied to Oil and Gas in Portuguese, highlighting domain-specific challenges and data scarcity issues.

Findings

Deep learning NLP techniques are effective for O&G text analysis.

Portuguese language presents unique challenges for NLP in O&G.

Limited public corpora hinder NLP advancements in this domain.

Abstract

Over the last few decades, institutions around the world have been challenged to deal with the sheer volume of information captured in unstructured formats, especially in textual documents. The so called Digital Transformation age, characterized by important technological advances and the advent of disruptive methods in Artificial Intelligence, offers opportunities to make better use of this information. Recent techniques in Natural Language Processing (NLP) with Deep Learning approaches allow to efficiently process a large volume of data in order to obtain relevant information, to identify patterns, classify text, among other applications. In this context, the highly technical vocabulary of Oil and Gas (O&G) domain represents a challenge for these NLP algorithms, in which terms can assume a very different meaning in relation to common sense understanding. The search for suitable mathematical representations and specific models requires a large amount of representative corpora in the O&G domain. However, public access to this material is scarce in the scientific literature, especially considering the Portuguese language. This paper presents a literature review about the main techniques for deep learning NLP and their major applications for O&G domain in Portuguese.