id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020875-vd4rtxmz	Suwaileh, Reem	Time-Critical Geolocation for Social Good	2020-03-24		.txt	text/plain	2030	134	51	To address this problem, I aim to exploit different techniques such as training neural models, enriching the tweet representation, and studying methods to mitigate the lack of labeled data. In my work, I am interested in tackling the Location Mention Prediction (LMP) problem during time-critical situations. The location taggers have to address many challenges including microblogging-specific challenges (e.g., tweet sparsity, noisiness, stream rapid-changing, hashtag riding, etc.) and the task-specific challenges (e.g., time-criticality of the solution, scarcity of labeled data, etc.). Alternatively, Sultanik and Fink [25] , used Information Retrieval (IR) based approach to identify the location mentions in tweets. Moreover, Hoang and Mothe [8] combined syntactic and semantic features to train traditional ML-based models whereas Kumar and Singh [13] trained a Convolutional Neural Network (CNN) model that learns the continuous representation of tweet text and then identifies the location mentions.	./cache/cord-020875-vd4rtxmz.txt	./txt/cord-020875-vd4rtxmz.txt