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Open Access

Topic-Modeling- and Subject-Classification-Analyses of Articles from the EURASIP Journal on Advances in Signal Processing

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Files

model-final.twords_w_stemming.txt (topic modeling with stemming) (4.06 KB)
 model-final.twords_wo_stemming.txt (topic modeling without stemming) (4.35 KB)
 Annif.png (subject classification Annif prototype API English (Annif.org)) (79.76 KB)
 fastText.png (subject classification fastText English (Annif.org)) (73.37 KB)
 Maui.png (subject classification Maui English (Annif.org)) (63.05 KB)

Date

2019-10-09

Type

Text
Image

Authors

Stille, Wolfgang
Freund, Jens

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

This data set contains the results of topic-modeling- and subject- classification-analyses of the abstracts of 87 articles from the EURASIP Journal on Advances in Signal Processing (ISSN: 1687-6180). All of the selected articles had in common that they were assigned the keyword “OFDM” (Orthogonal Frequency-Division Multiplexing) by the authors or the publisher. The topic modeling analyses were carried out with the program GibbsLDA++ (<http://gibbslda.sourceforge.net>) once with and once without stemming (model-final.twords_w_stemming.txt and model-final.twords_wo_stemming.txt, respectively). The program parameters were set to: src/lda -est -alpha 0.5 -beta 0.1 -ntopics 10 -niters 1000 -savestep 100 -twords 20 The subject classification analyses were carried out with the web-application Annif.org (<http://annif.org/>), which offers different algorithms for the classification. The following algorithms were used (the name of the corresponding result file is given in brackets): Annif prototype API English (Annif.png), fastText English (fastText.png), Maui English (Maui.png), TF-IDF English (TF-IDF.png), YSO ensemble English (YSO.png). A list with the DOIs of the articles can be found in the file "DOIs_analyzed_articles.txt" and the analyzed abstracts of these articles in the zip archive "Abstracts_EURASIPJAdvSignalProcess.zip".

Keywords

TDM, Text and Data Mining, Topic Modeling, Subject Classification, OFDM, Orthogonal Frequency-Division Multiplexing, Orthogonales Frequenzmultiplexverfahren

Citation

Identifier

https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2090
 http://dx.doi.org/10.25534/tudatalib-110

Endorsement

DFG Classification

1.14-03 Angewandte Sprachwissenschaften, Computerlinguistik
4.42-02 Kommunikationstechnik und- netze, Hochfrequenztechnik und photonische Systeme, Signalverarbeitung und maschinelles Lernen für Informationstechnik

Project(s)

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Collections

License

CC BY 4.0 - Attribution 4.0 International
Except where otherwise noted, this license is described as CC BY 4.0 - Attribution 4.0 International

Version History

Now showing 1 - 2 of 2
VersionDateSummary
5
 2019-11-26 13:11:16
We repeated the TDM analyses because we had noticed that at least 7 of the 87 abstracts analyzed had been incomplete in the first version of the dataset. A more detailed error description can be found in the description section of the new version.
1*
 2019-10-09 14:51:54
* Selected version