Journal article 779 views 147 downloads
VNLP: Visible natural language processing
Information Visualization, Volume: 20, Issue: 4, Pages: 245 - 262
Swansea University Authors: Mohammad Alharbi Alharbi, Matt Roach , Tom Cheesman, Bob Laramee
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DOI (Published version): 10.1177/14738716211038898
Abstract
In general, Natural Language Processing (NLP) algorithms exhibit black- box behavior.Users input text and output is provided with no explanation of how the results are obtained.In order to increase understanding and trust, users value transparent processing which may explain derived results and enab...
Published in: | Information Visualization |
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ISSN: | 1473-8716 1473-8724 |
Published: |
SAGE Publications
2021
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Online Access: |
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URI: | https://cronfa.swan.ac.uk/Record/cronfa57876 |
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Abstract: |
In general, Natural Language Processing (NLP) algorithms exhibit black- box behavior.Users input text and output is provided with no explanation of how the results are obtained.In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines.Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps.We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) design is applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert. |
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Keywords: |
Text alignment, parallel translations, text visualization |
College: |
Faculty of Science and Engineering |
Issue: |
4 |
Start Page: |
245 |
End Page: |
262 |