Resume Information Extraction via Post-OCR Text Processing
Selahattin Serdar Helli Senem Tanberk Sena Nur Cavsak
Information extraction (IE), one of the main tasks of natural language
processing (NLP), has recently increased importance in the use of resumes. In
studies on the text to extract information from the CV, sentence classification
was generally made using NLP models. In this study, it is aimed to extract
information by classifying all of the text groups after pre-processing such as
Optical Character Recognition (OCT) and object recognition with the YOLOv8
model of the resumes. The text dataset consists of 286 resumes collected for 5
different (education, experience, talent, personal and language) job
descriptions in the IT industry. The dataset created for object recognition
consists of 1198 resumes, which were collected from the open-source internet
and labeled as sets of text. BERT, BERT-t, DistilBERT, RoBERTa and XLNet were
used as models. F1 score variances were used to compare the model results. In
addition, the YOLOv8 model has also been reported comparatively in itself. As a
result of the comparison, DistilBERT was showed better results despite having a
lower number of parameters than other models.