@inproceedings{zeng-etal-2018-topic,
title = "Topic Memory Networks for Short Text Classification",
author = "Zeng, Jichuan and
Li, Jing and
Song, Yan and
Gao, Cuiyun and
Lyu, Michael R. and
King, Irwin",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1351",
doi = "10.18653/v1/D18-1351",
pages = "3120--3131",
abstract = "Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.",
}
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<abstract>Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.</abstract>
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%0 Conference Proceedings
%T Topic Memory Networks for Short Text Classification
%A Zeng, Jichuan
%A Li, Jing
%A Song, Yan
%A Gao, Cuiyun
%A Lyu, Michael R.
%A King, Irwin
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zeng-etal-2018-topic
%X Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.
%R 10.18653/v1/D18-1351
%U https://aclanthology.org/D18-1351
%U https://doi.org/10.18653/v1/D18-1351
%P 3120-3131
Markdown (Informal)
[Topic Memory Networks for Short Text Classification](https://aclanthology.org/D18-1351) (Zeng et al., EMNLP 2018)
ACL
- Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, and Irwin King. 2018. Topic Memory Networks for Short Text Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3120–3131, Brussels, Belgium. Association for Computational Linguistics.