Add to ORKGWe present the time-dependent topic-cluster model, a hierarchical approach for combining Latent Dirichlet Allocation and clustering via the Recurrent Chinese Restaurant Process. It inherits the advantages of both of its constituents, namely interpretability and concise representation. We show how it can be applied to streaming collec-tions of objects such as real world feeds in a news portal. We provide details of a parallel Sequen-tial Monte Carlo algorithm to perform inference in the resulting graphical model which scales to hundred of thousands of documents.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP ...
textDigital media collections hold an unprecedented source of knowledge and data about the world. Y...
We consider the problem of modeling temporal textual data taking endogenous and exogenous processes ...
We present the time-dependent topic-cluster model, a hierarchical approach for combining Latent Diri...
Clusters in document streams, such as online news articles, can be induced by their textual contents...
International audienceInformation spread on networks can be efficiently modeled by considering three...
Topic models based on latent Dirichlet allocation (LDA) assume a predefined vocabulary. This is reas...
A challenge created by the recent development in information technology is that people are often fac...
In this paper, I apply latent dirichlet allocation(LDA) to cluster 100,000 health related articles u...
Latent topic analysis has emerged as one of the most effective methods for classifying, clustering a...
The sizes of modern digital libraries have grown beyond our capacity to comprehend manually. Thus we...
The client of the project has problems with complex queries and noisewhen querying their stream of fi...
Structured probabilistic inference has shown to be useful in modeling complex latent structures of d...
International audienceThe textual content of a document and its publication date are intertwined. Fo...
People are increasingly relying on the Web and social media to find solutions to their problems in a...
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP ...
textDigital media collections hold an unprecedented source of knowledge and data about the world. Y...
We consider the problem of modeling temporal textual data taking endogenous and exogenous processes ...
We present the time-dependent topic-cluster model, a hierarchical approach for combining Latent Diri...
Clusters in document streams, such as online news articles, can be induced by their textual contents...
International audienceInformation spread on networks can be efficiently modeled by considering three...
Topic models based on latent Dirichlet allocation (LDA) assume a predefined vocabulary. This is reas...
A challenge created by the recent development in information technology is that people are often fac...
In this paper, I apply latent dirichlet allocation(LDA) to cluster 100,000 health related articles u...
Latent topic analysis has emerged as one of the most effective methods for classifying, clustering a...
The sizes of modern digital libraries have grown beyond our capacity to comprehend manually. Thus we...
The client of the project has problems with complex queries and noisewhen querying their stream of fi...
Structured probabilistic inference has shown to be useful in modeling complex latent structures of d...
International audienceThe textual content of a document and its publication date are intertwined. Fo...
People are increasingly relying on the Web and social media to find solutions to their problems in a...
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP ...
textDigital media collections hold an unprecedented source of knowledge and data about the world. Y...
We consider the problem of modeling temporal textual data taking endogenous and exogenous processes ...