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Topic-Modeling- and Subject-Classification-Analyses of Articles from the EURASIP Journal on Advances in Signal Processing
| dc.contributor.author | Stille, Wolfgang | |
| dc.contributor.author | Freund, Jens | |
| dc.date.accessioned | 2019-10-09T12:51:54Z | |
| dc.date.available | 2019-10-09T12:51:54Z | |
| dc.date.issued | 2019-09 | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2090 | |
| dc.identifier.uri | http://dx.doi.org/10.25534/tudatalib-110 | |
| dc.description | <p>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.</p> <p>The topic modeling analyses were carried out with the program GibbsLDA++ (<a href="http://gibbslda.sourceforge.net">http://gibbslda.sourceforge.net</a>) 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</p> <p>The subject classification analyses were carried out with the web-application Annif.org (<a href="http://annif.org/">http://annif.org/</a>), 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).</p> <p>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".</p> | en_US |
| dc.language.iso | en | en_US |
| dc.relation.isbasedon | EURASIP Journal on Advances in Signal Processing, ISSN: 1687-6180 | |
| dc.rights | Creative Commons Attribution 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | TDM | en_US |
| dc.subject | Text and Data Mining | en_US |
| dc.subject | Topic Modeling | en_US |
| dc.subject | Subject Classification | en_US |
| dc.subject | OFDM | en_US |
| dc.subject | Orthogonal Frequency-Division Multiplexing | en_US |
| dc.subject | Orthogonales Frequenzmultiplexverfahren | en_US |
| dc.subject.classification | 1.14-03 Angewandte Sprachwissenschaften, Computerlinguistik | en_US |
| dc.subject.classification | 4.42-02 Kommunikationstechnik und- netze, Hochfrequenztechnik und photonische Systeme, Signalverarbeitung und maschinelles Lernen für Informationstechnik | en_US |
| dc.subject.ddc | 621.3 | |
| dc.subject.ddc | 400 | |
| dc.title | Topic-Modeling- and Subject-Classification-Analyses of Articles from the EURASIP Journal on Advances in Signal Processing | en_US |
| dc.type | Text | en_US |
| dc.type | Image | en_US |
| tud.history.classification | Version=2020-2024;104-04 Angewandte Sprachwissenschaften, Experimentelle Linguistik, Computerlinguistik | |
| tud.history.classification | Version=2020-2024;408-02 Nachrichten- und Hochfrequenztechnik, Kommunikationstechnik und -netze, Theoretische Elektrotechnik |
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