学術雑誌論文 Relation Prediction in Multilingual Data Based on Multimodal Relational Topic Models

Sakata, Yosuke  ,  Eguchi, Koji

E100D ( 4 )  , pp.741 - 749 , 2017-04 , Institute of Electronics, Information and Communication Engineers(IEICE)
ISSN:1745136117451361
内容記述
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.
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http://www.lib.kobe-u.ac.jp/repository/90004293.pdf

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