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Named Entity Recognition (NER) is a task in Natural Language Processing (NLP) that aims to classify words into a predetermined list of Named Entities (NE). Many architectures have produced good results on high resourced languages like English and Chinese. However, the NER task has not yet achieved much progress for Bangla, a low resource Language. In this paper, we perform the NER task on Bangla Language using Word2Vec and contextual Bidirectional Encoder Representations from Transformers (BERT) embeddings. We propose multiple BERT-based deep learning models that use the contextualized embedding from BERT as inputs and a simple statistical approach for class weight cost sensitive learning. The modified cost-sensitive loss function was used to address the class imbalance of the data. In our modified cost-sensitive loss function, we penalize the dominant classes by taking the ratio concerning the maximum sample in a class instead of the whole dataset. This penalty is made so that the learner learns slowly for the dominant class. In addition, we experiment by adding a Conditional Random Field (CRF) layer and incorporating Focal Loss to the training process. We found the best F1 Macro score to be 65.96%, F1 Micro score of 90.64%, and F1 Message Understanding Coreference (MUC) score of 72.04%, which were calculated at Named Entity level. Our experimental results demonstrate that one of the proposed models, which jointly optimizes for the CRF loss and class weighted cost-sensitive loss according to our proposed statistical approach, achieve an improvement of over 8% F1 MUC score on a recently introduced Bangla NER dataset when compared to previously published work.

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Bloody tears (Haemolacria) are a rare symptom that can be caused by local or systemic pathology. It is one of the most alarming symptom in ophthalmology. Besides those, idiopathic cases have been reported. A case of hyperthyroidism where haemolacria was secondary to the condition has also been reported. Haemolacria are also reported as secondary to epistaxis. Psychogenic causes are described including Munchausen Syndrome by proxy. Here we describe a series of four cases of haemolacria along with bleeding from other sites, found as associated features of dissociative disorders. In this series, patients with age ranging from 14-17 years, three of them are female and one male have been included. Examination excluded local ocular and nasal pathology, coagulopathy and hyperthyroidism. In course of their illness, two of these cases met the DSM 5 criteria for both dissociative disorders and conversion disorders, rest of them have been diagnosed as mixed dissociative disorders. After appropriate intervention, three patients recovered completely and in one patient symptoms (also bleeding) recurred on re-exposure to the previous stress factors.We report three cases of Dissociative disorders and one with both dissociative and conversion disorder where bloody tears were one of the feature. To the best of our knowledge this is the first official report of its kind in Bangladesh. (J Bangladesh Coll