Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features Named entity recognition (NER) is a fundamental step for many natural language processing tasks and hence enhancing the performance of NER models is always appreciated. With limited resources being available, NER for SouthEast Asian languages like Telugu is quite a challenging problem. This paper attempts to improve the NER performance for Telugu using gazetteerrelated features, which are automatically generated using Wikipedia pages. We make use of these gazetteer features along with other wellknown features like contextual, wordlevel, and corpus features to build NER models. NER models are developed using three wellknown classifiersxe2x80x94conditional random field (CRF), support vector machine (SVM), and margin infused relaxed algorithms (MIRA). The gazetteer features are shown to improve the performance, and theMIRAbased NER model fared better than its counterparts SVM and CRF.

Named Entity Recognition for Nepali Language Named Entity Recognition (NER) has been studied for many languages like English, German, Spanish, and others but virtually no studies have focused on the Nepali language. One key reason is the lack of an appropriate, annotated dataset. In this paper, we describe a Nepali NER dataset that we created. We discuss and compare the performance of various machine learning models on this dataset. We also propose a novel NER scheme for Nepali and show that this scheme, based on graphemelevel representations, outperforms characterlevel representations when combined with BiLSTM models. Our best models obtain an overall F1 score of 86.89, which is a significant improvement on previously reported performance in literature.