Natural language processing for automated detection of incidental durotomy

2020 ◽  
Vol 20 (5) ◽  
pp. 695-700 ◽  
Author(s):  
Aditya V. Karhade ◽  
Michiel E.R. Bongers ◽  
Olivier Q. Groot ◽  
Erick R. Kazarian ◽  
Thomas D. Cha ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automated Detection ◽  
Incidental Durotomy

scholarly journals Cross-linguistic automated detection of metaphors for poverty and cancer

2018 ◽  
Vol 10 (3) ◽  
pp. 467-493 ◽  
Author(s):  
OANA DAVID ◽  
TEENIE MATLOCK
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Social Problems ◽  
Conceptual Metaphor ◽  
Automated Detection ◽  
Analytic Tool ◽  
Corpus Linguistic ◽  
Recent Developments ◽  
Corpus Data

abstractConceptual metaphor research has benefited from advances in discourse analytic and corpus linguistic methodologies over the years, especially given recent developments with Natural Language Processing (NLP) technologies. Such technologies are now capable of identifying metaphoric expressions across large bodies of text. Here we focus on how one particular analytic tool, MetaNet, can be used to study everyday discourse about personal and social problems, in particular, poverty and cancer, by leveraging reusable networks of primary metaphors enhanced with specific metaphor subcases. We discuss the advantages of this approach in allowing us to gain valuable insights into cross-linguistic metaphor commonalities and variation. To demonstrate its utility, we analyze corpus data from English and Spanish.

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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