natural-language-processing-2

Short Text Clustering

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Discovering New Intents with Deep Aligned Clustering

Hanlei Zhang, Hua Xu, Ting-En Lin, Rui Lv•Tue Dec 15 2020

This work proposes an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data, and leverages a few labeled known intent samples as prior knowledge to pre-train the model.

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Twin Contrastive Learning for Online Clustering

Mouxing Yang, Yunfan Li, Xiaocui Peng, Dezhong Peng, Taihao Li, Jian-Tao Huang•Fri Jul 08 2022

It is found that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.

144 0

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Supporting Clustering with Contrastive Learning

Shang-Wen Li, Ramesh Nallapati, Bing Xiang, K. McKeown, Dejiao Zhang, Feng Nan, Xiaokai Wei, Henghui Zhu, Andrew O. Arnold•Tue Mar 23 2021

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.

233 0

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Self-Taught Convolutional Neural Networks for Short Text Clustering

Jun Zhao, Jiaming Xu, Bo Xu, Peng Wang, Suncong Zheng, Guanhua Tian•Sat Dec 31 2016

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.

234 0

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A Self-Training Approach for Short Text Clustering

Chris Develder, Amir Hadifar, Lucas Sterckx, T. Demeester•Wed Jul 31 2019

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.

88 0

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Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Hanlei Zhang, Hua Xu, Ting-En Lin•Tue Nov 19 2019

Constrained deep adaptive clustering with cluster refinement (CDAC+) is proposed, an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process.

119 0

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Enhancement of Short Text Clustering by Iterative Classification

E. Milios, Md. Rashadul Hasan Rakib, N. Zeh, Magdalena Jankowska•Thu Jan 30 2020

The proposed clustering enhancement method not only improves the clustering quality of different baseline clustering methods but also outperforms the state-of-the-art short text clustering method on several short text datasets by a statistically significant margin.

45 0

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Intent Mining from past conversations for Conversational Agent

Ajay Chatterjee, Shubhashis Sengupta•Thu May 21 2020

This paper presents an intent discovery framework that can mine a vast amount of conversational logs and to generate labeled data sets for training intent models, and introduced an extension to the DBSCAN algorithm and a density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering.

46 0

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Efficient sparse spherical k-means for document clustering

Johannes Knittel, Steffen Koch, Thomas Ertl•Thu Jul 29 2021

This work proposes an efficient indexing structure to improve the scalability of Spherical k-Means with respect to k and exploits the sparsity of the input vectors and the convergence behavior of k- Means to reduce the number of comparisons on each iteration significantly.

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