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KNN-Based Pseudo-supervised RCNN Framework for Text Clustering

  • Zhi ChenEmail author
  • Wu GuoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

This paper explores the application of recurrent convolutional neural networks (RCNN) to text clustering, an unsupervised task in natural language processing (NLP). The RCNN is trained with pseudo-labels that are generated by pre-clustering on unsupervised document representations. To enhance the quality of pseudo-labels, the K-Nearest Neighbors (KNN) algorithm is used to select training samples for the neural network. After the deep feature representations of all documents have been obtained using the trained RCNN, the agglomerative hierarchical clustering (AHC) algorithm is used to cluster them. The experimental results on two public databases show that the proposed approach significantly boosts the performance of text clustering.

Keywords

Text clustering Pseudo-supervised K nearest neighbors 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Engineering Laboratory for Speech and Language Information ProcessingUniversity of Science and Technology of ChinaHefeiChina

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