DocumentCode :
573339
Title :
Adaptive Employee Profile Classification for Resource Planning Tool
Author :
Gonzalez, Tere ; Santos, Pano ; Orozco, Fernando ; Alcaraz, Mildreth ; Zaldivar, Victor ; Obeso, A.D. ; Garcia, Alan
Author_Institution :
Inf. Analytics Lab., HP Labs., Palo Alto, CA, USA
fYear :
2012
fDate :
24-27 July 2012
Firstpage :
544
Lastpage :
553
Abstract :
Matching the right people to the right job considering constraints such as qualifications, availability and cost is the cornerstone of IT projects delivery services. We present a study to improve data accuracy and completeness for resource matching by integrating unstructured data sources and introducing text mining techniques to dynamically adapt resource profile for resource planning decisions. Our approach discovers resource categories by extracting and learning new patterns from employee resumes; and incorporating resource experience for the job-matching optimization during the resource planning exercise.
Keywords :
data mining; optimisation; personnel; recruitment; text analysis; IT projects delivery services; adaptive employee profile classification; data accuracy; job-matching optimization; resource planning tool; text mining techniques; unstructured data sources; Dictionaries; Ontologies; Planning; Resumes; Text mining; Resource planning; bag of words; information extraction; naïve Bayes classifier; nlp; nltk; ontology; optimization; relation discovery; supervised classification; text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
SRII Global Conference (SRII), 2012 Annual
Conference_Location :
San Jose, CA
ISSN :
2166-0778
Print_ISBN :
978-1-4673-2318-5
Electronic_ISBN :
2166-0778
Type :
conf
DOI :
10.1109/SRII.2012.67
Filename :
6311037
Link To Document :
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