Text classification model fastText-Trendi-Topics 1.0

  Authors

Kuzman, Taja ; et al.show everyone

Kuzman, Taja ; Čibej, Jaka ; Ljubešić, Nikola ; Kosem, Iztok ; Ponikvar, Primož ; Dobrovoljc, Kaja ; Krek, Simon

  Item identifier

http://hdl.handle.net/11356/1710

 Project URL

https://sled.ijs.si/

 Referenced by

https://nl.ijs.si/jtdh22/pdf/JTDH2022_Kosem-et-al_Spremljevalni-korpus-Trendi.pdf

 Date issued

2022-10-28

 Type

toolService

 Language(s)

Slovenian

 Description

The fastText-Trendi-Topics model is a text classification model for categorizing news texts with one of 13 topic labels. It was trained on a set of approx. 36,000 Slovene texts from various Slovene news sources included in the Trendi Monitor Corpus of Slovene (http://hdl.handle.net/11356/1590) such as "rtvslo.si", "sta.si", "delo.si", "dnevnik.si", "vecer.com", "24ur.com", "siol.net", "gorenjskiglas.si", etc. The texts were semi-automatically categorized into 13 categories based on the sections under which they were published (i.e. URLs). The set of labels was developed in accordance with related categorization schemas used in other corpora and comprises the following topics: "črna kronika" (crime and accidents), "gospodarstvo, posel, finance" (economy, business, finance), "izobraževanje" (education), "okolje" (environment), "prosti čas" (free time), "šport" (sport), "umetnost, kultura" (art, culture), "vreme" (weather), "zabava" (entertainment), "zdravje" (health), "znanost in tehnologija" (science and technology), "politika" (politics), and "družba" (society). The categorization process is explained in more detail in Kosem et al. (2022): https://nl.ijs.si/jtdh22/pdf/JTDH2022_Kosem-et-al_Spremljevalni-korpus-Trendi.pdf The model was trained on the labeled texts using the word embeddings CLARIN.SI-embed.sl 1.0 (http://hdl.handle.net/11356/1204) and validated on a development set of 1,293 texts using the fastText library, 1000 epochs, and default values for the rest of the hyperparameters (see https://github.com/TajaKuzman/FastText-Classification-SLED for the full code). The model achieves a macro-F1-score of 0.85 on a test set of 1,295 texts (best for "vreme" at 0.97, worst for "prosti čas" at 0.67). Please note that the SloBERTa-Trendi-Topics 1.0 text classification model is also available (http://hdl.handle.net/11356/1709) that achieves higher classification accuracy, but is slower and computationally more demanding.

 Publisher

Jožef Stefan Institute

 Acknowledgement

Ministry of Culture of the Republic of Slovenia JR-infrastruktura-SJ-2021-2022 "SLED - Monitor corpus of Slovene and related resources"

University of Ljubljana I0-0022 "Network of Research Infrastructure Centres (MRIC)"

ARRS (Slovenian Research Agency) P6-0411 "Language Resources and Technologies for Slovene"

 Subject(s)

text classification Slovenian news articles fastText topic classification

 Collection(s)

CLARIN.SI data & tools

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