Open AccessResume extraction with conditional random field method
Author(s) -
Jason Anggakusuma,
Viny Christanti Mawardi,
Manatap Dolok Lauro
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1007/1/012154
Subject(s) - conditional random field , computer science , information extraction , precision and recall , information retrieval , process (computing) , annotation , field (mathematics) , artificial intelligence , mathematics , pure mathematics , operating system
Information extraction resume is a system used to carry out the automatic information extraction process on resumes of prospective employees to obtain key information including information on names, skills, educational experience, work experience, awards which are the main components in a curriculum vitae of prospective employees. Conditional Random Field (CRF) is one method that can be used to extract information. The step taken in the information extraction process is an annotation documents, tokenization, labeling on each token in the document, feature extraction, and the formation of models that will be used at the classification stage. In this study a system was built that could be used to extract CVs from users in pdf format. This study uses a CV from LinkedIn. Information extraction is done using 15 features and 11 classes. The evaluation of information extraction system for the employee’s resume has a precision value of 85,052%, a recall value of 85,052%, and an f-measure of 78,527%.