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DICE @ TREC-IS 2018: Combining Knowledge Graphs and Deep Learning to Identify Crisis-Relevant Tweets

Hamada M. Zahera, Rricha Jalota, Ricardo Usbeck
2018 Text Retrieval Conference  
Our TREC-IS results indicate that a model based on combining knowledge graphs (i.e., Babelfy), word embeddings and textual features outperformes classical machine learning models.  ...  In this paper, we describe our submissions to the TREC Incident Stream (TREC-IS) challenge 2018.  ...  Acknowledgements This work has been supported by the BMVI projects LIMBO (project no. 19F2029C), and also by the German Federal Ministry of Education and Research (BMBF) within 'KMU-innovativ: Forschung  ... 
dblp:conf/trec/ZaheraJU18 fatcat:cixxtlcjurh3tafv5nnqv3ev3y

I-AID: Identifying Actionable Information from Disaster-related Tweets [article]

Hamada M. Zahera, Rricha Jalota, Mohamed A. Sherif, Axel N. Ngomo
2021 arXiv   pre-print
However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task.  ...  Our results indicate that I-AID outperforms state-of-the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.  ...  Our task is to learn a multi-label classifier M : T → {0, 1} k that maps tweets T to relevant labels from Λ.  ... 
arXiv:2008.13544v2 fatcat:6yjmtfq4tfec5pf6uly2cpgzoe

Διερεύνηση της γεωλογικής δομής και της τεκτονικής παραμόρφωσης της Σερβομακεδονικής Μάζας στα όρη των Κερδυλλίων και Βερτίσκου (Βόρεια Ελλάδα)

Αναστάσιος Π. Πλούγαρλης
The study area is located in northern Greece and occupies the Vertiskos and Kerdyllion Mountains.  ...  Thus, the Kerdyllion Unit is comprised of rocks such as marbles but also biotite and banded amphibolite gneisses, in which strong signs of anatexis and migmatization were found, forming leucosomes and  ...  multi-task learning fashion. 2.3.2 Deep Learning Methods Zhang et al. (2016) investigate the task of keyphrase extraction from tweets.  ... 
doi:10.26262/ fatcat:snneggenqzdnvbl7kn7zpztqqq