We present our results for the shared tasks 2, 4 and 9 at the SMM4H Workshop at COLING 2022 achieved by succesfully fine-tuning pre-trained language models to the downstream tasks. We identify the occurence of code-switching in the test data for task 2 as a possible source of considerable performance degradation on the test set scores. We successfully exploit structural linguistic similarities in the datasets of tasks 4 and 9 for training on joined datasets, scoring first in task 9 and on par with SOTA in task 4.

This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing.