Comparative Analysis of RNN, LSTM, Bi-LSTM Performance for Location and Time Entity Recognition in Forest Fire Texts
Author:
Affiliation:
1. Amikom University Yogyakarta,Magister of Informatics Engineering,Yogyakarta,Indonesia
Funder
European Commission
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10497456/10497457/10497508.pdf?arnumber=10497508
Reference27 articles.
1. Dampak kebakaran hutan dan penegakan hukum [The impact of forest fires and law enforcement];Pasai;Jurnal Pahlawan,2020
2. Model Sistem Deteksi Dini Kebakaran Hutan dan Lahan (Karhutla) Berbasis Android di Kabupaten Pelalawan [Early Fire Detection System Model for Forest and Land Fires (Karhutla) Based on Android in Pelalawan District];Fatayat;Simtika,2020
3. BIDIRECTIONAL LSTM-CNNs UNTUK EKSTRAKSI ENTITY LOKASI KEBAKARAN PADA BERITA ONLINE BERBAHASA INDONESIA
4. LSTM Based Named Entity Chunking and Entity Extraction
5. Application of named entity recognition method for Indonesian datasets: a review
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