Twitter allows users to easily post tweets on any subject or event anytime, generating massive amounts of rich text content diverse topics. Automated methods such as Named Entity Recognition (NER) are required process the tweet data. Processing tweets, however, poses a special challenge they informal posts with incomplete context and often contain acronyms, hashtags, misspellings, abbreviations...

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most existing benchmarks lack domain-specialized types or do not focus on a certain domain, leading less effective cross-domain evaluation. To address these obstacles, we introduce dataset (CrossNER), fully-labeled collection data spanning over five diverse doma...

Named entity recognition (NER) is an important task in natural language processing, as it widely featured a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection Western and Asian languages. However, little effort has been to develop techniques the Urdu language, which prominent South hundreds millions speakers across globe. consi...

Stemming is the process of reducing words to their stems or roots. Due to the morphological richness and complexity of the Arabic language, stemming is an essential part of most Natural Language Processing (NLP) tasks for this language. In this paper, we study the impact of different stemming approaches on the Named Entity Recognition (NER) task for Arabic and explore the merits, limitations an...