Before revision:  Our system combines state-of-the-art approaches to process and represents textual data in order to capture its underlying semantics . In particular, we use 1) Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to learn a contextualized vector representation of a tweet, and 2) a graph-based architecture to compute semantic correlations between the entities and hashtags in tweets and their corresponding labels .
After revision:  I-AID incorporates three main components: i) a BERT-based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets' words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding labels .