Cross-lingual link prediction using multimodal relational topic models

• Published in: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) pp. 1 - 8
• Main Authors: Sakata, Yosuke, Eguchi, Koji
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Analytical models; Automobiles; Data models; Gaussian distribution; Graphical models; Predictive models; Resource management
• DOI: 10.1109/ICIS.2016.7550883

There are increasing demands for improved analysis of multimodal data that consist of multiple representations, such as multilingual documents and text-annotated images. One promising approach for analyzing such multimodal data is latent topic models. In this paper, we propose conditionally independent generalized relational topic models (CI-gRTM) for predicting unknown relations across different multiple representations of multimodal data. We developed CI-gRTM as a multimodal extension of discriminative relational topic models called generalized relational topic models (gRTM). We demonstrated through experiments with multilingual documents that CI-gRTM can more effectively predict both multilingual representations and relations between two different language representations compared with several state-of-the-art baseline models that enable to predict either multilingual representations or unimodal relations.