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Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques

2023-01-01 01:36:32
Muhammad Suleman, Muhammad Asif, Tayyab Zamir, Ayaz Mehmood, Jebran Khan, Nasir Ahmad, Kashif Ahmad
arXiv_CL
           
Abstract

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.

Abstract (translated)

URL

https://arxiv.org/abs/2301.00321

PDF

https://arxiv.org/pdf/2301.00321.pdf

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