IEICE TRANSACTIONS on Information
Short Text Classification Based on Distributional Representations of Words
Summary :
Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.10 pp.2562-2565
- Publication Date
- 2016/10/01
- Publicized
- 2016/07/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016SLL0006
- Type of Manuscript
- Special Section LETTER (Special Section on Recent Advances in Machine Learning for Spoken Language Processing)
- Category
- Text classification
Authors
Chenglong MA
Chinese Academy of Sciences
Qingwei ZHAO
Chinese Academy of Sciences
Jielin PAN
Chinese Academy of Sciences
Yonghong YAN
Chinese Academy of Sciences
Keyword
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Chenglong MA, Qingwei ZHAO, Jielin PAN, Yonghong YAN, "Short Text Classification Based on Distributional Representations of Words" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 10, pp. 2562-2565, October 2016, doi: 10.1587/transinf.2016SLL0006.
Abstract: Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016SLL0006/_p
Copy
@ARTICLE{e99-d_10_2562,
author={Chenglong MA, Qingwei ZHAO, Jielin PAN, Yonghong YAN, },
journal={IEICE TRANSACTIONS on Information},
title={Short Text Classification Based on Distributional Representations of Words},
year={2016},
volume={E99-D},
number={10},
pages={2562-2565},
abstract={Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transinf.2016SLL0006},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Short Text Classification Based on Distributional Representations of Words
T2 - IEICE TRANSACTIONS on Information
SP - 2562
EP - 2565
AU - Chenglong MA
AU - Qingwei ZHAO
AU - Jielin PAN
AU - Yonghong YAN
PY - 2016
DO - 10.1587/transinf.2016SLL0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2016
AB - Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.
ER -
English
