Indonesian disaster named entity recognition from multi source information using bidirectional LSTM (BiLSTM)

Precise logistic support is essential after a disaster occurs. It must be timely, accurate, targeted, and based on existing needs. However, obtaining sufficient and accurate information related to logistic distribution locations remains a key problem. Therefore, implementing Named Entity Recognition...

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主要な著者:	Guruh Fajar Shidik, Filmada Ocky Saputra, Galuh Wilujeng Saraswati, Nurul Anisa Sri Winarsih, Muhammad Syaifur Rohman, Ricardus Anggi Pramunendar, Edi Jaya Kusuma, Danny Oka Ratmana, Valentijn Venus, Pulung Nurtantio Andono, Zainal Arifin Hasibuan
フォーマット:	Artigo
言語:	Inglês
出版事項:	Elsevier 2024-09-01
シリーズ:	Journal of Open Innovation: Technology, Market and Complexity
主題:	Disaster emergency information Named entity recognition Deep learning BiLSTM networks Random oversampling
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S2199853124001525
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