Named entity recognition (NER) models based on neural language models (LMs) exhibit stateof-the-art performance. However, the performance of such LMs have not been studied in detail with respect to finer language related aspects in the context of NER tasks. Such a study will be helpful in effective application of these models for cross-lingual and multilingual NER tasks. In this study, we examine the effects of script, vocabulary sharing, foreign names and pooling of multilanguage training data for building NER models. It is observed that monolingual BERT embeddings show the highest recognition accuracy among all transformerbased LMs for monolingual NER models. It is also seen that vocabulary sharing and data augmentation with foreign named entities (NEs) are most effective towards improving accuracy of cross-lingual NER models. Multilingual NER models trained by pooling data from similar languages can address training data inadequacy and exhibit performance close to that of monolingual models trained with adequate NER-tagged data of a single language.
he exact detection and creation of treatment regimens for conditions like diabetic retinopathy and hypertensive retinopathy depend on the segmentation of retinal blood vessels. Methods based on deep learning have been employed in the last ten years to segment blood vessels in fundus images. Due to the lack of uniform data in large quantities, the wide range of brightness and anatomical structures of the fundus images that are available, and the variety of shapes and sizes of the vessels in the tree-like vascular structure, it is still difficult to accurately segment all the vessels in a retinal fundus image. In this study, we present a unique lightweight CNN with an encoder-decoder structure for real-time and precise segmentation of blood vessels. The most popular retina datasets, DRIVE and CHASE, were used to train and evaluate the model. With an accuracy of 96.3% and 78.45% f1 score with respect to the DRIVE dataset and accuracy of 97.14% and 82.79% f1 score with respect to the CHASE dataset, we can observe that the model is lightweight and has provided comparable performance. Additionally, the suggested model runs faster with an average inference time of 0.0059 seconds and has fewer parameters than state-of-the-art models currently in use.
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