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    <abstract>Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification.</abstract>
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%0 Conference Proceedings
%T Initializing Convolutional Filters with Semantic Features for Text Classification
%A Li, Shen
%A Zhao, Zhe
%A Liu, Tao
%A Hu, Renfen
%A Du, Xiaoyong
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F li-etal-2017-initializing
%X Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification.
%R 10.18653/v1/D17-1201
%U https://aclanthology.org/D17-1201
%U https://doi.org/10.18653/v1/D17-1201
%P 1884-1889

Markdown (Informal)

[Initializing Convolutional Filters with Semantic Features for Text Classification](https://aclanthology.org/D17-1201) (Li et al., EMNLP 2017)

• Initializing Convolutional Filters with Semantic Features for Text Classification (Li et al., EMNLP 2017)

ACL

• Shen Li, Zhe Zhao, Tao Liu, Renfen Hu, and Xiaoyong Du. 2017. Initializing Convolutional Filters with Semantic Features for Text Classification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1884–1889, Copenhagen, Denmark. Association for Computational Linguistics.