What do users think about Virtual Reality relaxation applications? A mixed methods study of online user reviews using natural language processing

Abstract This study aims to construct a corpus-informed list of Arabic Formulaic Sequences (ArFSs) for use in language pedagogy (LP) and Natural Language Processing (NLP) applications. A hybrid mixed methods model was adopted for extracting ArFSs from a corpus, that combined automatic and manual extracting methods, based on well-established quantitative and qualitative criteria that are relevant from the perspective of LP and NLP. The pedagogical implications of this list are examined to facilitate the inclusion of ArFSs in the process of learning and teaching Arabic, particularly for non-native speakers. The computational implications of the ArFSs list are related to the key role of the ArFSs as a novel language resource in the improvement of various Arabic NLP tasks.

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Scientific Articles Exploration System Model based in Immersive Virtual Reality and Natural Language Processing Techniques

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2018.090736 ◽

2018 ◽

Vol 9 (7) ◽

Author(s):

Luis Alfaro ◽

Ricardo Linares ◽

Jose Herrera

Keyword(s):

Virtual Reality ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

System Model ◽

Immersive Virtual Reality ◽

Model Based ◽

Processing Techniques

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Accelerating Mixed Methods Research With Natural Language Processing of Big Text Data

Journal of Mixed Methods Research ◽

10.1177/15586898211021196 ◽

2021 ◽

pp. 155868982110211

Author(s):

Tammy Chang ◽

Melissa DeJonckheere ◽

V. G. Vinod Vydiswaran ◽

Jiazhao Li ◽

Lorraine R. Buis ◽

...

Keyword(s):

Natural Language Processing ◽

Mixed Methods ◽

Natural Language ◽

Language Processing ◽

Mixed Methods Research ◽

Innovative Technology ◽

Education Economics ◽

Text Data ◽

Textual Data ◽

Promising Solution

Situations of catastrophic social change, such as COVID-19, raise complex, interdisciplinary research questions that intersect health, education, economics, psychology, and social behavior and require mixed methods research. The pandemic has been a quickly evolving phenomenon, which pressures the time necessary to perform mixed methods research. Natural language processing (NLP) is a promising solution that leverages computational approaches to analyze textual data in “natural language.” The aim of this article is to introduce NLP as an innovative technology to assist with the rapid mixed methods analysis of textual big data in times of catastrophic change. The contribution of this article is illustrating how NLP is a type of mixed methods analysis and making recommendations for its use in mixed methods research.

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Natural Language Processing and Enhanced Clinical Decision Making Radiology and VINCI

PsycEXTRA Dataset ◽

10.1037/e615572012-015 ◽

2012 ◽

Author(s):

Eliot Siegel

Keyword(s):

Decision Making ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Decision Making ◽

Clinical Decision

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Natural Language Processing in the Clinical Setting

PsycEXTRA Dataset ◽

10.1037/e615572012-013 ◽

2012 ◽

Author(s):

Thomas H. Payne

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Setting

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A Review and evaluation of Machine Translation methods for Lumasaaba

Journal of Digital Science ◽

10.33847/2686-8296.2.1_1 ◽

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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