TY - GEN
T1 - A novel knowledge extraction framework for resumes based on text classifier
AU - Chen, Jie
AU - Niu, Zhendong
AU - Fu, Hongping
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In the information age, there are plenty of resume data in the internet. Several previous research have been proposed to extract facts from resumes, however, they mainly rely on large amounts of labeled data and the text format information, which made them limited by human efforts and the file format. In this paper, we propose a novel framework, not depending on the file format, to extract knowledge about the person for building a structured resume repository. The proposed framework includes two major processes: the first is to segment text into semistructured data with some text pretreatment operations. The second is to further extract knowledge from the semi-structured data with text classifier. The experiments on the real dataset demonstrate the improvement when compared to previous researches.
AB - In the information age, there are plenty of resume data in the internet. Several previous research have been proposed to extract facts from resumes, however, they mainly rely on large amounts of labeled data and the text format information, which made them limited by human efforts and the file format. In this paper, we propose a novel framework, not depending on the file format, to extract knowledge about the person for building a structured resume repository. The proposed framework includes two major processes: the first is to segment text into semistructured data with some text pretreatment operations. The second is to further extract knowledge from the semi-structured data with text classifier. The experiments on the real dataset demonstrate the improvement when compared to previous researches.
KW - Knowledge Extraction
KW - Resume fact extraction
KW - Text classifier
UR - https://www.scopus.com/pages/publications/84937406927
U2 - 10.1007/978-3-319-21042-1_58
DO - 10.1007/978-3-319-21042-1_58
M3 - Conference contribution
AN - SCOPUS:84937406927
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 540
EP - 543
BT - Web-Age Information Management - 16th International Conference, WAIM 2015, Proceedings
A2 - Sun, Yizhou
A2 - Li, Jian
PB - Springer Verlag
T2 - 16th International Conference on Web-Age Information Management, WAIM 2015
Y2 - 8 June 2015 through 10 June 2015
ER -