Depression is a common and serious mental illness that early detection can improve the patient’s symptoms and make depression easier to treat. This paper mainly introduces the relevant content of the task “Detecting Signs of Depression from Social Media Text at DepSign-LT-EDI@ACL-2022”. The goal of DepSign is to classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed” based on social media’s posts. In this paper, we propose a predictive ensemble model that utilizes the fine-tuned contextualized word embedding, ALBERT, DistilBERT, RoBERTa, and BERT base model. We show that our model outperforms the baseline models in all considered metrics and achieves an F1 score of 54% and accuracy of 61%, ranking 5th on the leader-board for the DepSign task.

In this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021. In this task, systems should determine whether a word that occurs in two different sentences is used with the same meaning or not. We proposed several methods using a pre-trained BERT model. In two of them, we paraphrased sentences and add them as input to the BERT, and in one of them, we used WordNet to add some extra lexical information. We evaluated our proposed methods on test data in SemEval- 2021 task 2.