Abstract
Text segmentation is very important for many fields including information retrieval,summarization,language modeling,anaphora resolution and so on.Text segmentation based on LDA models corpora and texts with LDA.Parameters are estimated with Gibbs sampling of MCMC and the word probability is represented.Different latent topics are associated with observable words.In the experiments,Chinese whole sentences are taken as elementary blocks.Variety of similarity metrics and several approaches of discovering boundaries are tried.The best results show the right combination of them can make the error rate far lower than other algorithms of text segmentation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
