Abstract
The aim of this article is to show that the document embedding using the doc2vec algorithm can substantially improve the performance of the standard method for unsupervised document classification – the K-means clustering. We have performed rather extensive set of experiments on one English and two Czech datasets and the results suggest that representing the documents using vectors generated by the doc2vec algorithm brings a consistent improvement across languages and datasets. The English dataset – 20NewsGroups – was processed in a way that allows direct comparison with the results of both supervised and unsupervised algorithms published previously. Such comparison is provided in the paper, together with the results of supervised classification achieved by the state-of-the-art SVM classifier.
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Notes
- 1.
This data set can be found at http://qwone.com/~jason/20Newsgroups/ and it was originally collected by Ken Lang.
- 2.
It was created from a database of news articles downloaded from the http://www.ceskenoviny.cz/ at the University of West Bohemia and constitutes only a small fraction of the entire database – the description of the full database can be found in [14].
- 3.
Created by colleagues at University of West Bohemia.
- 4.
ufal.morphodita at https://pypi.python.org/pypi/ufal.morphodita.
- 5.
More precisely the TfidfVectorizer module from that package.
- 6.
Applying lemmatization and data-driven stop-word removal.
- 7.
Use of LSA method.
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This research was supported by the Ministry of Education, Youth and Sports of the Czech Republic project No. LO1506.
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Novotný, J., Ircing, P. (2018). The Benefit of Document Embedding in Unsupervised Document Classification. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_49
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