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Three Dimensions of Reproducibility in Natural Language Processing.
K Bretonnel Cohen1,2, Jingbo Xia3, Pierre Zweigenbaum2
1Computational Bioscience Program, University of Colorado School of Medicine.
LREC ... International Conference on Language Resources & Evaluation : [Proceedings]. International Conference on Language Resources & Evaluation
|June 19, 2018
Summary
This study proposes a new ontology for reproducibility in natural language processing (NLP) research. It defines three dimensions—conclusion, finding, and value—to improve study comparison and meta-analyses.
Area of Science:
- Natural Language Processing (NLP)
- Scientific Research Methodology
Background:
- Reproducibility is a critical issue in scientific research, yet a consensus definition is lacking.
- This ambiguity hinders the comparison of studies and a clear understanding of reproducibility in NLP.
Purpose of the Study:
- To propose a novel ontology for reproducibility in Natural Language Processing (NLP).
- To enhance future research, communication, and retrospective meta-analyses in NLP.
- To provide a framework for understanding different types of reproducibility claims.
Main Methods:
- Development of a three-dimensional ontology for reproducibility.
- The dimensions correspond to reproducibility of a conclusion, a finding, and a value.
- Analysis of three biomedical NLP papers using the proposed ontology.
Main Results:
- The proposed ontology accounts for various types of research reports in NLP.
- Demonstrated the application of the ontology to analyze specific NLP studies.
- Highlighted the utility of the defined dimensions for assessing reproducibility.
Conclusions:
- The proposed ontology offers a structured approach to defining and assessing reproducibility in NLP.
- This framework facilitates clearer communication and more robust meta-analyses.
- Standardizing the definition of reproducibility is crucial for advancing NLP research.
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