Multimodal Topic Labelling

Ionut Sorodoc, Jey Han Lau, Nikolaos Aletras, Timothy Baldwin


Abstract
Topics generated by topic models are typically presented as a list of topic terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. Traditionally, topic label systems focus on a single label modality, e.g. textual labels. In this work we propose a multimodal approach to topic labelling using a simple feedforward neural network. Given a topic and a candidate image or textual label, our method automatically generates a rating for the label, relative to the topic. Experiments show that this multimodal approach outperforms single-modality topic labelling systems.
Anthology ID:
E17-2111
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
701–706
Language:
URL:
https://aclanthology.org/E17-2111
DOI:
Bibkey:
Cite (ACL):
Ionut Sorodoc, Jey Han Lau, Nikolaos Aletras, and Timothy Baldwin. 2017. Multimodal Topic Labelling. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 701–706, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Multimodal Topic Labelling (Sorodoc et al., EACL 2017)
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PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/E17-2111.pdf