Abstract: This paper describes the submission to SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) by team SAB. This task aims to encourage growth in the field of Named Entity Recognition (NER) by focusing on complex and difficult categories of entities, in 12 different language tracks. The task of NER has historically shown the best results when a model incorporates an external knowledge base or gazetteer, however, less research has been applied to examining the effects of incorporating linguistic information into the model. In this task, we explored combining NER, part-of-speech (POS), and dependency relation labels into a multi-task model and report on the findings. We determine that the addition of POS and dependency relation information in this manner does not improve results.