2012/04/11 by Khalid El-Arini, El-Arini, Khalid, Emily B. Fox +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.CL #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1204.2523
arxiv created 2012/04/11 · openalex publication_date 2012/04/11 · arxiv updated 2012/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In information retrieval, a fundamental goal is to transform a document into concepts that are representative of its content. The term "representative" is in itself challenging to define, and various tasks require different granularities of concepts. In this paper, we aim to model concepts that are sparse over the vocabulary, and that flexibly adapt their content based on other relevant semantic information such as textual structure or associated image features. We explore a Bayesian nonparametric model based on nested beta processes that allows for inferring an unknown number of strictly sparse concepts. The resulting model provides an inherently different representation of concepts than a standard LDA (or HDP) based topic model, and allows for direct incorporation of semantic features. We demonstrate the utility of this representation on multilingual blog data and the Congressional Record.