Harnessing the Power of Natural Language Processing and Fuzzy Theory to Improve Oil and Gas Data Management Efficiency

2019 ◽  
Author(s):  
Hasan Asfoor ◽  
Walid Kaskas
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Data Management ◽  
Language Processing ◽  
Oil And Gas ◽  
Fuzzy Theory ◽  
Management Efficiency

scholarly journals Bootstrapping the Language Archive

2011 ◽  
Vol 6 ◽  
Author(s):  
Steven Bird
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Data Management ◽  
Language Processing ◽  
Field Data ◽  
Management Tools ◽  
New Methods ◽  
Language Technology ◽  
Processing Techniques

There are grounds to believe that language technology in general, and natural language processing in particular, have important roles to play in creating and analyzing corpora for small languages. This goes beyond the development of data management tools to the application of natural language processing techniques to small and noisy datasets, and the design of new methods that operate within the constraints of linguistic field data. A set of seven such constraints (or "axioms for scalable work with small languages") are presented, and suggestions for further NLP research are related back to these axioms.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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