Reproducibility dataset for a large experimental survey on word embeddings and ontology-based methods for word similarity
Identifieur interne : 000604 ( Main/Exploration ); précédent : 000603; suivant : 000605Reproducibility dataset for a large experimental survey on word embeddings and ontology-based methods for word similarity
Auteurs : Juan J. Lastra-Díaz [Espagne] ; Josu Goikoetxea [Espagne] ; Mohamed Ali Hadj Taieb [Tunisie] ; Ana García-Serrano [Espagne] ; Mohamed Ben Aouicha [Tunisie] ; Eneko Agirre [Espagne]Source :
- Data in Brief [ 2352-3409 ] ; 2019.
Abstract
This data article introduces a reproducibility dataset with the aim of allowing the exact replication of all experiments, results and data tables introduced in our companion paper (Lastra-Díaz et al., 2019), which introduces the largest experimental survey on ontology-based semantic similarity methods and Word Embeddings (WE) for word similarity reported in the literature. The implementation of all our experiments, as well as the gathering of all raw data derived from them, was based on the software implementation and evaluation of all methods in HESML library (Lastra-Díaz et al., 2017), and their subsequent recording with Reprozip (Chirigati et al., 2016). Raw data is made up by a collection of data files gathering the raw word-similarity values returned by each method for each word pair evaluated in any benchmark. Raw data files were processed by running a R-language script with the aim of computing all evaluation metrics reported in (Lastra-Díaz et al., 2019), such as Pearson and Spearman correlation, harmonic score and statistical significance p-values, as well as to generate automatically all data tables shown in our companion paper. Our dataset provides all input data files, resources and complementary software tools to reproduce from scratch all our experimental data, statistical analysis and reported data. Finally, our reproducibility dataset provides a self-contained experimentation platform which allows to run new word similarity benchmarks by setting up new experiments including other unconsidered methods or word similarity benchmarks.
Url:
DOI: 10.1016/j.dib.2019.104432
PubMed: 31516953
PubMed Central: 6736772
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>This data article introduces a reproducibility dataset with the aim of allowing the exact replication of all experiments, results and data tables introduced in our companion paper (Lastra-Díaz et al., 2019), which introduces the largest experimental survey on ontology-based semantic similarity methods and Word Embeddings (WE) for word similarity reported in the literature. The implementation of all our experiments, as well as the gathering of all raw data derived from them, was based on the software implementation and evaluation of all methods in HESML library (Lastra-Díaz et al., 2017), and their subsequent recording with Reprozip (Chirigati et al., 2016). Raw data is made up by a collection of data files gathering the raw word-similarity values returned by each method for each word pair evaluated in any benchmark. Raw data files were processed by running a R-language script with the aim of computing all evaluation metrics reported in (Lastra-Díaz et al., 2019), such as Pearson and Spearman correlation, harmonic score and statistical significance p-values, as well as to generate automatically all data tables shown in our companion paper. Our dataset provides all input data files, resources and complementary software tools to reproduce from scratch all our experimental data, statistical analysis and reported data. Finally, our reproducibility dataset provides a self-contained experimentation platform which allows to run new word similarity benchmarks by setting up new experiments including other unconsidered methods or word similarity benchmarks.</p>
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<author><name sortKey="Reichart, R" uniqKey="Reichart R">R. Reichart</name>
</author>
<author><name sortKey="Rappoport, A" uniqKey="Rappoport A">A. Rappoport</name>
</author>
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<biblStruct><analytic><author><name sortKey="Szumlanski, S R" uniqKey="Szumlanski S">S.R. Szumlanski</name>
</author>
<author><name sortKey="Gomez, F" uniqKey="Gomez F">F. Gomez</name>
</author>
<author><name sortKey="Sims, V K" uniqKey="Sims V">V.K. Sims</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Wieting, J" uniqKey="Wieting J">J. Wieting</name>
</author>
<author><name sortKey="Bansal, M" uniqKey="Bansal M">M. Bansal</name>
</author>
<author><name sortKey="Gimpel, K" uniqKey="Gimpel K">K. Gimpel</name>
</author>
<author><name sortKey="Livescu, K" uniqKey="Livescu K">K. Livescu</name>
</author>
<author><name sortKey="Roth, D" uniqKey="Roth D">D. Roth</name>
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</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Yang, D" uniqKey="Yang D">D. Yang</name>
</author>
<author><name sortKey="Powers, D M" uniqKey="Powers D">D.M. Powers</name>
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<affiliations><list><country><li>Espagne</li>
<li>Tunisie</li>
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<tree><country name="Espagne"><noRegion><name sortKey="Lastra Diaz, Juan J" sort="Lastra Diaz, Juan J" uniqKey="Lastra Diaz J" first="Juan J." last="Lastra-Díaz">Juan J. Lastra-Díaz</name>
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<name sortKey="Agirre, Eneko" sort="Agirre, Eneko" uniqKey="Agirre E" first="Eneko" last="Agirre">Eneko Agirre</name>
<name sortKey="Garcia Serrano, Ana" sort="Garcia Serrano, Ana" uniqKey="Garcia Serrano A" first="Ana" last="García-Serrano">Ana García-Serrano</name>
<name sortKey="Goikoetxea, Josu" sort="Goikoetxea, Josu" uniqKey="Goikoetxea J" first="Josu" last="Goikoetxea">Josu Goikoetxea</name>
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<country name="Tunisie"><noRegion><name sortKey="Hadj Taieb, Mohamed Ali" sort="Hadj Taieb, Mohamed Ali" uniqKey="Hadj Taieb M" first="Mohamed Ali" last="Hadj Taieb">Mohamed Ali Hadj Taieb</name>
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<name sortKey="Aouicha, Mohamed Ben" sort="Aouicha, Mohamed Ben" uniqKey="Aouicha M" first="Mohamed Ben" last="Aouicha">Mohamed Ben Aouicha</name>
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