3/11/2024 0 Comments Time crisis 2 rom![]() The proposed CNN, along with a conditional random field at the last layer, is found to perform better than other models, with an ![]() ![]() Several deep learning-based models, including convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and attention-based Bi-LSTM, are implemented on real-world English and Hindi language tweets to determine their suitability for extracting location references. This article presents a technique based on deep neural networks for extracting geographical references mentioned in bilingual tweets. As a result, determining the geographical location of the tweet is a challenging problem. The extraction of geographical information from tweet text is limited by the fact that individuals frequently publish multilingual tweets that contain numerous grammatical and spelling errors, as well as nonstandard acronyms. Geotagged tweets are extremely infrequent, and other location fields, such as user location and place name, are unreliable. Geographical location information about users and events is critical in these scenarios. Twitter is increasingly being used during disasters to communicate with authorities, ascertain the ground reality, and coordinate real-time rescue and recovery activities.
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