Paper: Topic Models, Latent Space Models, Sparse Coding, and All That: A Systematic Understanding of Probabilistic Semantic Extraction in Large Corpus

ACL ID P12-4003
Title Topic Models, Latent Space Models, Sparse Coding, and All That: A Systematic Understanding of Probabilistic Semantic Extraction in Large Corpus
Venue Annual Meeting of the Association of Computational Linguistics
Session Tutorial Abstracts
Year 2012
Authors

Tutorial Abstracts of ACL 2012, page 3, Jeju, Republic of Korea, 8 July 2012. c?2012 Association for Computational Linguistics Topic Models, Latent Space Models, Sparse Coding, and All That: A systematic understanding of probabilistic semantic extraction in large corpus Eric Xing School of Computer Science Carnegie Mellon University Abstract Probabilistic topic models have recently gained much popularity in informational re- trieval and related areas. Via such mod- els, one can project high-dimensional objects such as text documents into a low dimen- sional space where their latent semantics are captured and modeled; can integrate multiple sources of information?to ?share statistical strength? among components of a hierarchical probabilistic model; and can structurally dis- play and classi...