  Named entity recognition (NER) is used to extract information from various
documents and texts such as names and dates. It is important to extract
education and work experience information from resumes in order to filter them.
Considering the fact that all information in a resume has to be entered to the
companys system manually, automatizing this process will save time of the
companies. In this study, a deep learning-based semi-automatic named entity
recognition system has been implemented with a focus on resumes in the field of
IT. Firstly, resumes of employees from five different IT related fields has
been annotated. Six transformer based pre-trained models have been adapted to
named entity recognition problem using the annotated data. These models have
been selected among popular models in the natural language processing field.
The obtained system can recognize eight different entity types which are city,
date, degree, diploma major, job title, language, country and skill. Models
used in the experiments are compared using micro, macro and weighted F1 scores
and the performance of the methods was evaluated. Taking these scores into
account for test set the best micro and weighted F1 score is obtained by
RoBERTa and the best macro F1 score is obtained by Electra model.
