Representation learning for very short texts using weighted word embedding aggregation
- Author
- Cedric De Boom (UGent) , Steven Van Canneyt (UGent) , Thomas Demeester (UGent) and Bart Dhoedt (UGent)
- Organization
- Keywords
- IBCN, Natural language processing, Information storage and retrieval, Artificial intelligence, Word embeddings, Representation learning
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8023857
- MLA
- De Boom, Cedric et al. “Representation Learning for Very Short Texts Using Weighted Word Embedding Aggregation.” PATTERN RECOGNITION LETTERS 80 (2016): 150–156. Print.
- 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.
- 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: 150–156.
- 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.
- 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. Elsevier; 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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