• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.75-78
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• 2001

본 논문은 통계청에서 실시하는 인구주택 총조사로부터 획득된 각 개인의 직업 및 직종을 기술하고 있는 자연어를 입력받아 입력된 자연어가 의미하는 한국 표준 산업/구업 분류 코드의 후보들을 생성하는 산업/직업 코드 분류 도구를 제안한다. 코드 분류는 분류할 코드를 문서 범주로 간주하면 문서 분류와 동일한 문제로 생각할 수 있다. 하지만 본 산업/직업 코드 분류 문제는 입력되는 자연어의 길이가 한 두 문장 정도로 매우 짧아 문서 분류에 사용될 자질들이 개수가 주어 기존의 문서 분류 기법을 적용하기 어렵다. 이에 본 논문은 표준 코드를 기술하고 있는 내용을 미리 색인하고 입력된 자연어로부터 질의어를 생성하여 벡터공간모델로 질의어를 검색후 질의어와 일치율이 가장 높은 코드들을 분류될 후보 코드로 계시하는 정보검색 기법을 이용한 산업/직업 코드 분류 도구를 개발하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.594-601
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• 2006

Coding of occupational and industrial codes is a major operation in census survey of Korean statistics bureau. The coding process has been done manually. Such manual work is very labor and cost intensive and it usually causes inconsistent results. This paper proposes an automatic coding system based on example-based learning. The system converts natural language input into corresponding numeric codes using code generation system trained by example-based teaming after applying manually built rules. As experimental results performed with training data consisted of 400,000 records and 260 manual rules, the proposed system showed about 76.69% and 99.68% accuracy for occupational code classification and industrial code classification, respectively.

• The Journal of Korean Association of Computer Education
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• v.7 no.4
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• pp.51-60
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• 2004

This paper proposes an automated coding system of Korean standard industry/occupation for census which reduces a lot of cost and labor for manual coding. The proposed system converts natural language responses on survey questionnaires into corresponding numeric codes using information retrieval techniques and document classification algorithm. The system was experimented with 46,762 industry records and occupation 36,286 records using 10-fold cross -validation evaluation method. As experimental results, the system show 87.08% and 66.08% production rates when classifying industry records into level 2 and level 5 codes respectively. The system shows slightly lower performances on occupation code classification. We expect that the system is enough to be used as a semi-automate coding system which can minimize manual coding task or as a verification tool for manual coding results though it has much room to be improved as an automated coding system.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.388-390
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• 2006

본 논문은 수동 코드 분류 규칙과 예제기반의 자동 학습을 이용하는 한국어 표준 산업/직업 코드 자동분류 시스템을 제안한다. 제안된 시스템은 산업과 직업에 대하여 설명하는 자연어를 입력받아 해당 산업/직업 분류 코드를 생성하는 시스템으로 수작업으로 구축된 규칙을 적용한 후 규칙이 적용되지 않는 레코드는 예제 기반의 학습을 이용한 자동 분류 시스템에 의해서 해당 코드를 할당한다.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.23-30
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• 2021

An Automated Industry and Occupation Coding System assigns statistical classification code to the enormous amount of natural language data collected from people who write about their industry and occupation. Unlike previous studies that applied information retrieval, we propose a system that does not need an index database and gives proper code regardless of the level of classification. Also, we show our model, which utilized KoBERT that achieves high performance in natural language downstream tasks with deep learning, outperforms baseline. Our method achieves 95.65%, 91.51%, and 97.66% in Occupation/Industry Code Classification of Population and Housing Census, and Industry Code Classification of Census on Basic Characteristics of Establishments. Moreover, we also demonstrate future improvements through error analysis in the respect of data and modeling.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.394-398
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• 2020

산업/직업분류 자동코딩시스템은 고용조사 등을 함에 있어 사업체 정보, 업무, 직급, 부서명 등 사용자의 다양한 입력을 표준 산업/직업분류에 맞춰 코드 정보를 제공해주는 시스템이다. 입력 데이터로부터 비지도학습 기반의 색인어 추출 모델을 학습하고, 부분단어 임베딩이 적용된 색인어 임베딩 모델을 통해 입력 벡터를 추출 후, 출력 분류 코드를 인코딩하여 지도학습 모델에서 학습하는 방법을 적용하였다. 기존 시스템의 분류 결과 데이터를 통해 대, 중, 소, 세분류에서 높은 정확도의 모델을 구축할 수 있으며, 기계학습 기술의 적용이 가능한 시스템임을 알 수 있다.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.169-179
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• 2005

Standard industry and occupation code are usually assigned manually in Korean census. The manual coding is very labor intensive and expensive task. Furthermore, inconsistent coding is resulted from the ability of human experts and their working environments. This paper proposes an automatic code classification system which converts natural language responses on survey questionnaires into corresponding numeric codes by using manually constructed rule base and example-based machine learning. The system was trained with 400,000 records of which standard codes was assigned. It was evaluated with 10-fold cross validation and was tested with three code sets: population occupation set, industry set, and industry survey set. The proposed system showed 76.63%, 82.24 and 99.68% accuracy for each code set.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.11a
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• pp.33-45
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• 2001

Korean standard industrial/occupational classification has been the basis of producing accurate statistical data related with our industrial structure and distribution of industry and occupation since 1960. But coding over several million records not only requires high cost in the aspects of time and manpower but also has many problems in accuracy and consistency. Therefore, we got to develop the automatic coding system in order to work out these problems of manual coding. This paper shows the structure of our system and the result of experiment over survey data of 2,000 Census.

• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.239-258
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• 2013

This study aims to examine that Jeollabuk-do 14 cities and counties's job creation policy would lead to a virtuous circle of the local economy through measuring the ratio of spatial mismatch. We suppose that the higher proportion of spatial mismatch in a city or county is, the lower multiplier effect contributes the local economy, especially in the income of residents and the influx of population. For the analysis, this study uses Korean Regional Employment Survey Micro-data and calculates the labor demand self-sufficiency(LDSS) rate of every local labor market for measuring the degree of spatial mismatch. Also we calculate the LDSS rate of employment status, industry, job classification and wage for testing the independency of local labor market. After analyzing, just Jeonju, Gunsan, Iksan, and Namwon could be regarded as independent local labor market where LDSS rates are above 75% in most criteria. But other local labor markets depend on outer labor supplies. For the development of regional economy, it is necessary to consider the creation of 'good jobs' that can induce the labor in parallel with the quantitative increase of employment.