  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.
