@InProceedings{lenc-kral:2017:RANLP,
  author    = {Lenc, Ladislav  and  Kral, Pavel},
  title     = {Word Embeddings for Multi-label Document Classification},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {431--437},
  abstract  = {In this paper, we analyze and evaluate word embeddings for representation of
	longer texts in the multi-label classification scenario. The embeddings are
	used in three convolutional neural network topologies. The experiments are
	realized on the Czech \v{C}TK and English Reuters-21578 standard corpora. We
	compare the results
	of word2vec static and trainable embeddings with randomly initialized word
	vectors. We conclude that initialization does not play an important role for
	classification. However, learning of word vectors is crucial to obtain good
	results.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_057}
}