• Home
  • Blog
  • People
  • Projects
  • Publications
  • Seminars
  • DSR Expo
  • Courses

Data Science Research

Menu
  • Home
  • Blog
  • People
  • Projects
  • Publications
  • Seminars
  • DSR Expo
  • Courses

Probabilistic Ensemble Fusion for Multimodal Word Sense Disambiguation

With the advent of abundant multimedia data on the Internet, there have been research efforts on multimodal machine learning to utilize data from different modalities Invitations birthday word available for free. Current approaches mostly focus on developing models to fuse low-level features from multiple modalities and learn unified representation from different modalities pc clean up free. But most related work failed to justify why we should use multimodal data and multimodal fusion, and few of them leveraged the complementary relation among different modalities rollercoaster tycoon 3 free full version nederlands.

In this paper, we first identify the correlative and complementary relations among multiple modalities. Then we propose a probabilistic ensemble fusion model to capture the complementary relation between two modalities (images and text) nero 8 kostenlos herunterladen. Experimental results on the UIUC-ISD dataset show our ensemble approach outperforms approaches using only single modality. Word sense disambiguation (WSD) is the use case we studied to demonstrate the effectiveness of our probabilistic ensemble fusion model wie kann ich sims herunterladen.

Authors: 
Yang Peng, Daisy Zhe Wang, Ishan Patwa, Dihong Gong, Chunsheng Victor Fang

Bibtex:

@inproceedings{peng2015probabilistic,
  title={Probabilistic Ensemble Fusion for Multimodal Word Sense Disambiguation},
  author={Peng, Yang and Wang, Daisy Zhe and Patwa, Ishan and Gong, Dihong and Fang, Chunsheng Victor},
  booktitle={2015 IEEE International Symposium on Multimedia (ISM)},
  pages={172--177},
  year={2015},
  organization={IEEE}
}

Download:
[pdf]

 

Sponsors

NIST

Adobe_Logo

DTCC

pcori uf-clinical

ICHP

Recent Posts

Recent Posts

  • A Brief Overview of Weak Supervision
  • DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
  • IDTrees Data Science Challenge: 2017
  • Efficient Conditional Rule Mining over Knowledge Bases
  • Taming The Data Monster To Make Better Decisions

Related Blogs

  • ampLab
  • Data Beta
  • Fast ML

Post Categories

Categories

  • courses
  • ecology
  • NIST and open eval
  • publications
  • research
  • research directions
  • survey
  • Uncategorized

Archives

Archives

  • October 2020
  • December 2019
  • April 2019
  • December 2018
  • August 2018
  • February 2018
  • November 2017
  • June 2017
  • May 2017
  • March 2017
  • December 2016
  • October 2016
  • April 2016
  • March 2016
  • December 2015
  • November 2015
  • October 2015
  • May 2015
  • November 2014
  • October 2014
  • July 2014
  • May 2014
  • March 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013

Meta

DSR Wiki
Site Admin
WordPress.org