%0 Conference Paper %1 JointModel2019 %A Zahera, Hamada M. %A Sherif, Mohamed Ahmed %A Ngonga Ngomo, Axel-Cyrille %B K-CAP 2019: Knowledge Capture Conference %D 2019 %K dice group_aksw limboproject ngonga opal sherif simba solide zahera %P 4 %T Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction %U https://svn-serv.cs.uni-paderborn.de/datascience/papers/2019/K-CAP19_Joint-model/public.pdf %X Existing technologies employ different machine learning approaches to predict disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. In this work, we consider social media as a supplementary source of knowledge in addition to historical environmental data. Further, we build a joint model that learns from disaster-related tweets and environmental data to improve prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with their semantics representations computed with the traditional word2vec. Our experiments show that our proposed approach outperforms the accuracy of state-of-the-art models in disaster prediction.

@inproceedings{JointModel2019, abstract = {Existing technologies employ different machine learning approaches to predict disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. In this work, we consider social media as a supplementary source of knowledge in addition to historical environmental data. Further, we build a joint model that learns from disaster-related tweets and environmental data to improve prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with their semantics representations computed with the traditional word2vec. Our experiments show that our proposed approach outperforms the accuracy of state-of-the-art models in disaster prediction.}, added-at = {2024-03-04T14:13:35.000+0100}, author = {Zahera, Hamada M. and Sherif, Mohamed Ahmed and {Ngonga Ngomo}, Axel-Cyrille}, biburl = {https://www.bibsonomy.org/bibtex/275f99a868d0a21313f8cbae23c5ec1a0/aksw}, booktitle = {K-CAP 2019: Knowledge Capture Conference}, interhash = {03fa7b1612f9a3a7b850bdd4033d4823}, intrahash = {75f99a868d0a21313f8cbae23c5ec1a0}, keywords = {dice group_aksw limboproject ngonga opal sherif simba solide zahera}, organization = {ACM}, pages = 4, timestamp = {2024-03-04T14:13:35.000+0100}, title = {{Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction}}, url = {https://svn-serv.cs.uni-paderborn.de/datascience/papers/2019/K-CAP19_Joint-model/public.pdf}, year = 2019 }