15 papers with code • 1 benchmarks • 2 datasets
Grouping a set of texts in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). (Source: Adapted from Wikipedia)
The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture.
In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data.
Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts.
Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).