Tech Reports
Tech Report kmi-10-02 Abstract
Bayesian Models for Sentence-Level Subjectivity Detection
Techreport ID: kmi-10-02
Date: 2010
Author(s): Yulan He
This paper proposes subjLDA for sentence-level subjectivity detection by modifying the latent Dirichlet allocation (LDA) model through adding an additional layer to model sentence-level subjectivity labels. A variant, called joint-subjLDA, has also been described. The model inference and parameter estimation algorithms, and Gibbs sampling procedure are presented.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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