Advertisement
Published in:
2020 | OriginalPaper | Chapter
Research on Chinese Short Text Clustering Ensemble via Convolutional Neural Networks
Authors : Haowen Wan, Bo Ning, Xiaoyu Tao, Jianfei Long
Published in: Artificial Intelligence in China
Publisher: Springer Singapore
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Different from traditional text, short texts are characterized by high dimensionality, sparseness, and large text size. At the same time, some existing clustering ensemble algorithms treat each clusters equally, which will lead to the problem that the clustering results are not good enough. To solve this problem, this paper proposed a short text clustering ensemble algorithm based on convolution neural network (CNN). Firstly, the word2vec model is used to preserve the semantic relationship between words and obtain the multi-dimensional word vector representation; secondly, the feature is extracted from the original vector combining with the CNN; thirdly, clustering methods are used to cluster vectors; and then finally, Gini coefficient is used to measure the reliability of clustering, and the final clustering ensemble is carried out.