Self-Taught convolutional neural networks for short text clustering.
TLDR
A flexible Self-Taught Convolutional neural network framework for Short Text Clustering, which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner is proposed.About:
This article is published in Neural Networks.The article was published on 2017-04-01 and is currently open access. It has received 166 citations till now. The article focuses on the topics: Cluster analysis & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering
TL;DR: The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features, and is compared with the other comparative algorithms published in the literature.
Proceedings ArticleDOI
Supporting Clustering with Contrastive Learning
Dejiao Zhang,Feng Nan,Xiaokai Wei,Shang-Wen Li,Henghui Zhu,Kathleen R. McKeown,Ramesh Nallapati,Andrew Arnold,Bing Xiang +8 more
TL;DR: This work proposes Supporting Clustering with Contrastive Learning (SCCL) – a novel framework to leverage contrastive learning to promote better separation in distance-based clustering and demonstrates the effectiveness of SCCL in leveraging the strengths of both bottom-up instance discrimination and top-down clustering to achieve better intra-clusters and inter-cluster distances.
Journal ArticleDOI
Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering
TL;DR: The functional selection technique has two aims: maximize text clustering algorithm reliability and minimize the number of uninformative traits and the proposed technique produces a mature convergence rate and requires minimal computational time and is trapped in local minima in a low dimensional space.
Proceedings ArticleDOI
A Self-Training Approach for Short Text Clustering
TL;DR: The method is proposed, which learns discriminative features from both an autoencoder and a sentence embedding, then uses assignments from a clustering algorithm as supervision to update weights of the encoder network.
Book ChapterDOI
Learning Convolutional Ranking-Score Function by Query Preference Regularization
TL;DR: A new ranking scoring function based on the convolutional neural network (CNN) is proposed, which has a structure of CNN, and its parameters are adjusted to both queries and query preferences.
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Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.