Year: 2019
Type: conference-paper
Abstract: In this paper, we describe the contribution of CU-RAISA team to the 2019 Madar shared task 2 1 , which focused on Twitter User finegrained dialect identification.Among participating teams, our system ranked the 4th (with 61.54%) F1-Macro measure.Our system is trained using a character level convolutional bidirectional long-short-term memory (BiL-STM) network trained on approximately 2k users' data.We show that training on concatenated user tweets as input is further superior to training on user tweets separately and assign user's label on the mode of user's tweets' predictions.
DOI: 10.18653/v1/w19-4636
Authors: Mohamed Elaraby, Ahmed Zahran
Institution: Cairo University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2970660698