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

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