Knowledge-based rules for the extraction of complex, fine-grained locative references from tweets

Autores/as

Resumen

The automatic analysis of user-generated text content from social media involves the challenge of extracting the locative references mentioned in microtexts, so that their geographic coordinates can be identified and the locations can be pinpointed on a map in geolocation systems. The goal of this article is to describe a knowledge-based model that captures a wide variety of locative references, ranging from geopolitical entities and natural landforms to points of interest and traffic ways.

Biografía del autor/a

Nicolás José Fernández Martínez, Universidad Católica San Antonio de Murcia

SPECIAL ISSUE

Citas

Ahlers, D. (2013). Assessment of the accuracy of GeoNames gazetteer data. In Proceedings of the 7th Workshop on Geographic Information Retrieval - GIR ’13 (pp. 74-81).

https://doi.org/10.1145/2533888.2533938

Ahmed, M. F., Vanajakshi, L., & Suriyanarayanan, R. (2019). Real-Time Traffic Congestion Information from Tweets Using Supervised and Unsupervised Machine Learning Techniques. Transportation in Developing Economies, 5(2). https://doi.org/10.1007/s40890-019-0088-2

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Publicado

2020-06-29

Número

Sección

Artículos Nuevos