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Event Geoparser with Pseudo-Location Entity Identification and Numerical Argument Extraction Implementation and Evaluation in Indonesian News Domain

1
School of Electrical and Informatics Engineering, Bandung Institute of Technology, Bandung 40132, Indonesia
2
University Center of Excellence on Artificial Intelligence for Vision, Natural Language Processing & Big Data Analytics (U-CoE AI-VLB), Bandung Institute of Technology, Bandung 40132, Indonesia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 712; https://doi.org/10.3390/ijgi9120712
Received: 9 November 2020 / Accepted: 18 November 2020 / Published: 28 November 2020
Geoparser is a fundamental component of a Geographic Information Retrieval (GIR) geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, geoparsing of news articles which report several events across many place-mentions in the document are not yet adequately handled by regular geoparser, where the scope of resolution is either toponym-level or document-level. The capacity to detect multiple events and geolocate their true coordinates along with their numerical arguments is still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose an event geoparser model with three stages of processing, which tightly integrates event extraction model into geoparsing and provides precise event-level resolution scope. The model casts the geotagging and event extraction as sequence labeling and uses LSTM-CRF inferencer equipped with features derived using Aggregated Topic Model from a large corpus to increase the generalizability. Throughout the proposed workflow and features, the geoparser is able to significantly improve the identification of pseudo-location entities, resulting in a 23.43% increase for weighted F1 score compared to baseline gazetteer and POS Tag features. As a side effect of event extraction, various numerical arguments are also extracted, and the output is easily projected to a rich choropleth map from a single news document. View Full-Text
Keywords: geoparser; geographic information retrieval; event extraction; argument extraction; information extraction; named entity recognition; conditional random function; lstm; semantic gazetteer; topic model geoparser; geographic information retrieval; event extraction; argument extraction; information extraction; named entity recognition; conditional random function; lstm; semantic gazetteer; topic model
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MDPI and ACS Style

Dewandaru, A.; Widyantoro, D.H.; Akbar, S. Event Geoparser with Pseudo-Location Entity Identification and Numerical Argument Extraction Implementation and Evaluation in Indonesian News Domain. ISPRS Int. J. Geo-Inf. 2020, 9, 712. https://doi.org/10.3390/ijgi9120712

AMA Style

Dewandaru A, Widyantoro DH, Akbar S. Event Geoparser with Pseudo-Location Entity Identification and Numerical Argument Extraction Implementation and Evaluation in Indonesian News Domain. ISPRS International Journal of Geo-Information. 2020; 9(12):712. https://doi.org/10.3390/ijgi9120712

Chicago/Turabian Style

Dewandaru, Agung, Dwi H. Widyantoro, and Saiful Akbar. 2020. "Event Geoparser with Pseudo-Location Entity Identification and Numerical Argument Extraction Implementation and Evaluation in Indonesian News Domain" ISPRS International Journal of Geo-Information 9, no. 12: 712. https://doi.org/10.3390/ijgi9120712

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