Improving Data Extraction System to Parse Data from Scraped Job Advertisements

Extracting the information from an online job advertisement might be a little tricky. The information is wrapped with redundant information, called boilerplate, that is not related to the job at all. The information also needs to be segmented and classified into the right class or groups. After the...

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Bibliographic Details
Published inJIRAE (International Journal of Industrial Research and Applied Engineering) (Online) Vol. 5; no. 1; pp. 19 - 22
Main Author Jason, Claudia Nathasia
Format Journal Article
LanguageEnglish
Published Petra Christian University 26.08.2021
Subjects
data classification
data parsing
data segmentation
feature extraction
job advertisement
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Summary:Extracting the information from an online job advertisement might be a little tricky. The information is wrapped with redundant information, called boilerplate, that is not related to the job at all. The information also needs to be segmented and classified into the right class or groups. After the information has been classified, it is easier to find the features (e.g., required skills and required education) that make the later processing faster.
ISSN:2407-7259
2407-7259
DOI:10.9744/jirae.5.1.19-22