Related papers: Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base

Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base

URL: http://arxiv.org/abs/2212.11856v1
Date: Thu, 22 Dec 2022 16:42:21 GMT
Title: Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base
Authors: V\'ictor Su\'arez-Paniagua and Steven Derby and Tri Kurniawan Wijaya
Abstract summary: Early detection of relevant locations in a piece of news is important in extreme events such as environmental disasters, war conflicts, disease outbreaks, or political turmoils. We propose a system that infers the relevant locations even when they are not mentioned explicitly in the text. We contribute to the research community with a gold-standard multilingual news-location dataset, NewsLOC.
Score: 0.7734726150561089
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Early detection of relevant locations in a piece of news is especially important in extreme events such as environmental disasters, war conflicts, disease outbreaks, or political turmoils. Additionally, this detection also helps recommender systems to promote relevant news based on user locations. Note that, when the relevant locations are not mentioned explicitly in the text, state-of-the-art methods typically fail to recognize them because these methods rely on syntactic recognition. In contrast, by incorporating a knowledge base and connecting entities with their locations, our system successfully infers the relevant locations even when they are not mentioned explicitly in the text. To evaluate the effectiveness of our approach, and due to the lack of datasets in this area, we also contribute to the research community with a gold-standard multilingual news-location dataset, NewsLOC. It contains the annotation of the relevant locations (and their WikiData IDs) of 600+ Wikinews articles in five different languages: English, French, German, Italian, and Spanish. Through experimental evaluations, we show that our proposed system outperforms the baselines and the fine-tuned version of the model using semi-supervised data that increases the classification rate. The source code and the NewsLOC dataset are publicly available for being used by the research community at https://github.com/vsuarezpaniagua/NewsLocation.

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