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Representation learning for very short texts using weighted word embedding aggregation

Cedric De Boom (UGent) , Steven Van Canneyt (UGent) , Thomas Demeester (UGent) and Bart Dhoedt (UGent)
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Keywords
IBCN, Natural language processing, Information storage and retrieval, Artificial intelligence, Word embeddings, Representation learning

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MLA
De Boom, Cedric, et al. “Representation Learning for Very Short Texts Using Weighted Word Embedding Aggregation.” PATTERN RECOGNITION LETTERS, vol. 80, Elsevier, 2016, pp. 150–56, doi:10.1016/j.patrec.2016.06.012.
APA
De Boom, C., Van Canneyt, S., Demeester, T., & Dhoedt, B. (2016). Representation learning for very short texts using weighted word embedding aggregation. PATTERN RECOGNITION LETTERS, 80, 150–156. https://doi.org/10.1016/j.patrec.2016.06.012
Chicago author-date
De Boom, Cedric, Steven Van Canneyt, Thomas Demeester, and Bart Dhoedt. 2016. “Representation Learning for Very Short Texts Using Weighted Word Embedding Aggregation.” PATTERN RECOGNITION LETTERS 80: 150–56. https://doi.org/10.1016/j.patrec.2016.06.012.
Chicago author-date (all authors)
De Boom, Cedric, Steven Van Canneyt, Thomas Demeester, and Bart Dhoedt. 2016. “Representation Learning for Very Short Texts Using Weighted Word Embedding Aggregation.” PATTERN RECOGNITION LETTERS 80: 150–156. doi:10.1016/j.patrec.2016.06.012.
Vancouver
1.
De Boom C, Van Canneyt S, Demeester T, Dhoedt B. Representation learning for very short texts using weighted word embedding aggregation. PATTERN RECOGNITION LETTERS. 2016;80:150–6.
IEEE
[1]
C. De Boom, S. Van Canneyt, T. Demeester, and B. Dhoedt, “Representation learning for very short texts using weighted word embedding aggregation,” PATTERN RECOGNITION LETTERS, vol. 80, pp. 150–156, 2016.
@article{8023857,
  author       = {{De Boom, Cedric and Van Canneyt, Steven and Demeester, Thomas and Dhoedt, Bart}},
  issn         = {{0167-8655}},
  journal      = {{PATTERN RECOGNITION LETTERS}},
  keywords     = {{IBCN,Natural language processing,Information storage and retrieval,Artificial intelligence,Word embeddings,Representation learning}},
  language     = {{eng}},
  pages        = {{150--156}},
  publisher    = {{Elsevier}},
  title        = {{Representation learning for very short texts using weighted word embedding aggregation}},
  url          = {{http://dx.doi.org/10.1016/j.patrec.2016.06.012}},
  volume       = {{80}},
  year         = {{2016}},
}

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