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Computer Science > Information Retrieval

arXiv:2211.08042 (cs)
[Submitted on 15 Nov 2022]

Title:MM-Locate-News: Multimodal Focus Location Estimation in News

Authors:Golsa Tahmasebzadeh, Eric Müller-Budack, Sherzod Hakimov, Ralph Ewerth
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Abstract:The consumption of news has changed significantly as the Web has become the most influential medium for information. To analyze and contextualize the large amount of news published every day, the geographic focus of an article is an important aspect in order to enable content-based news retrieval. There are methods and datasets for geolocation estimation from text or photos, but they are typically considered as separate tasks. However, the photo might lack geographical cues and text can include multiple locations, making it challenging to recognize the focus location using a single modality. In this paper, a novel dataset called Multimodal Focus Location of News (MM-Locate-News) is introduced. We evaluate state-of-the-art methods on the new benchmark dataset and suggest novel models to predict the focus location of news using both textual and image content. The experimental results show that the multimodal model outperforms unimodal models.
Subjects: Information Retrieval (cs.IR)
Cite as: arXiv:2211.08042 [cs.IR]
  (or arXiv:2211.08042v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2211.08042

Submission history

From: Golsa Tahmasebzadeh [view email]
[v1] Tue, 15 Nov 2022 10:47:45 UTC (5,460 KB)
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