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A Vector Semantics Approach to the Geoparsing Disambiguation Task for Texts in Spanish

9 pagesPublished: November 4, 2019

Abstract

Nowadays, online news sources generate continuous streams of information that includes references to real locations. Linking these locations to coordinates in a map usually requires two steps involving the named entity: extraction and disambiguation. In past years, efforts have been devoted mainly to the first task. Approaches to location disambiguation include knowledge-based, map-based and data-driven methods. In this paper, we present a work in progress for location disambiguation in news documents that uses a vector-semantic representation learned from information sources that include events and geographic descriptions, in order to obtain a ranking for the possible locations. We will describe the proposed method and the results obtained so far, as well as ideas for future work.

Keyphrases: doc2vec, embeddings, geoparsing, location disambiguation, nlp

In: Oscar S. Siordia, José Luis Silván Cárdenas, Alejandro Molina-Villegas, Gandhi Hernandez, Pablo Lopez-Ramirez, Rodrigo Tapia-McClung, Karime González Zuccolotto and Mario Chirinos Colunga (editors). Proceedings of the 1st International Conference on Geospatial Information Sciences, vol 13, pages 47-55.

BibTeX entry
@inproceedings{iGISc2019:Vector_Semantics_Approach_Geoparsing,
  author    = {Filomeno Alcántara and Alejandro Molina and Victor Muñiz},
  title     = {A Vector Semantics Approach to the Geoparsing Disambiguation Task for Texts in Spanish},
  booktitle = {Proceedings of the 1st International Conference on Geospatial Information Sciences},
  editor    = {Oscar S. Siordia and José Luis Silván Cárdenas and Alejandro Molina-Villegas and Gandhi Hernandez and Pablo Lopez-Ramirez and Rodrigo Tapia-McClung and Karime González Zuccolotto and Mario Chirinos Colunga},
  series    = {Kalpa Publications in Computing},
  volume    = {13},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/KbMg},
  doi       = {10.29007/pl5h},
  pages     = {47-55},
  year      = {2019}}
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