TY - GEN
T1 - An automatic coding system with a three-grade confidence level corresponding to the national/international occupation and industry standard
T2 - 6th International Conference on Knowledge Engineering and Ontology Development, KEOD 2014
AU - Takahashi, Kazuko
AU - Taki, Hirofumi
AU - Tanabe, Shunsuke
AU - Li, Wei
N1 - Publisher Copyright:
Copyright © 2014 SCITEPRESS - Science and Technology Publications All rights reserved.
PY - 2014
Y1 - 2014
N2 - We develop a new automatic coding system with a three-grade confidence level corresponding to each of the national/international standard code sets for answers to open-ended questions regarding to respondent's occupation and industry in social surveys including a national census. The "occupation and industry coding" is a necessary task for statistical processing. However, this task requires a great deal of labor and time-consuming. In addition, inconsistent results occur if the coders are not experts of coding. In formal research, various automatic coding systems have been developed, which are incomplete and generally unfriendly to a non-developer user. Our new system assigns three candidate codes to an answer for coders by SVMs (Support Vector Machines), and attaches a three-grade confidence level to the first-ranked predicted code by using classification scores to support a manual check of the results. The system is now open to the public through the Website of the Social Science Japan Data Archive (SSJDA). After the submitted data file which followed the specified format is approved, the users can obtain files of codes for up to four kinds with a three-grade confidence level. In this paper, we describe our system and evaluate it.
AB - We develop a new automatic coding system with a three-grade confidence level corresponding to each of the national/international standard code sets for answers to open-ended questions regarding to respondent's occupation and industry in social surveys including a national census. The "occupation and industry coding" is a necessary task for statistical processing. However, this task requires a great deal of labor and time-consuming. In addition, inconsistent results occur if the coders are not experts of coding. In formal research, various automatic coding systems have been developed, which are incomplete and generally unfriendly to a non-developer user. Our new system assigns three candidate codes to an answer for coders by SVMs (Support Vector Machines), and attaches a three-grade confidence level to the first-ranked predicted code by using classification scores to support a manual check of the results. The system is now open to the public through the Website of the Social Science Japan Data Archive (SSJDA). After the submitted data file which followed the specified format is approved, the users can obtain files of codes for up to four kinds with a three-grade confidence level. In this paper, we describe our system and evaluate it.
KW - Answers to open-ended question
KW - Automatic coding system
KW - Confidence level
KW - Machine learning
KW - Natural language processing
KW - Occupation and industry coding
UR - http://www.scopus.com/inward/record.url?scp=84909979953&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84909979953&partnerID=8YFLogxK
U2 - 10.5220/0005131703690375
DO - 10.5220/0005131703690375
M3 - Conference contribution
AN - SCOPUS:84909979953
T3 - KEOD 2014 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development
SP - 369
EP - 375
BT - KEOD 2014 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development
A2 - Filipe, Joaquim
A2 - Filipe, Joaquim
A2 - Dietz, Jan
A2 - Aveiro, David
PB - INSTICC Press
Y2 - 21 October 2014 through 24 October 2014
ER -