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- ONS Classification Coding Tools Project Occupation Classification Workshop RSS, London, 21 June 2004 Nigel Swier.

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Presentation on theme: "- ONS Classification Coding Tools Project Occupation Classification Workshop RSS, London, 21 June 2004 Nigel Swier."— Presentation transcript:

1 - ONS Classification Coding Tools Project Occupation Classification Workshop RSS, London, 21 June 2004 Nigel Swier

2 - Overview of ONS Coding Tools Project Aim: To select and operationalise a standard tool for assigning classification codes to verbatim text responses given in answer to a question Scope: For all classifications (except ICD10 for cause of death coding), including occupation (SOC) and industry (SIC) Both automatic and interactive coding functionality Development of selected tool into a component so that can be used within the new ONS technical architecture Context: Part of the ONS Statistical Infrastructure Development Project (itself part of the ONS Statistical Modernisation Programme).

3 - Office for National Statistics ONS formed in 1996 Central Statistics Office Office of Population Censuses and Surveys (OPCS) Employment Department

4 - Statistical Modernisation Programme (SMP) Inherited Infrastructure: Multiple databases Multiple development tools Proliferation of statistical tools and methods Poor metadata Paper-based dissemination Risky statistical systems ONS vision: Single repository (Oracle) Java (J2EE) Standard statistical tools and methods (e.g. coding tool) Corporate metadata system Web-based dissemination Robust statistical systems £ 75 million to deliver SMP ( )

5 - Data Collection Survey design Survey case management Statistical Value Chain Operations on Unit Data Editing Imputation Coding Dissemination Operations on Aggregate Data Time series Tabulation Disclosure Control Weighting Estimation ONS Metadata Repository Corporate ONS Repository for Data (CORD) Common ONS Statistical Tools

6 - Benefits of Statistical Modernisation Robust statistical systems Automated workflow: More rapid publishing of statistical outputs Improved efficiency Improved job satisfaction Data will be a corporate resource. Along with improved metadata it will allow ONS to leverage greater value from data holdings Reduced licencing and IT support costs Reduced staff training costs and easier transferability of staff

7 - Evaluation criteria Functionality –Automatic and interactive coding –Able to handle simple and complex classifications –Dependent coding Performance (coding/agreement rates) Technical (fit with new ONS technical environment) Supplier support Impact on ONS outputs

8 - Evaluating and selecting the tool Started (in earnest) January 2003 Establish detailed evaluation criteria Investigate tools and identify a shortlist (ACTR, PDC) Obtain software, preparation of knowledge bases for testing, Preparation of test data Testing (automatic coding performance) Analysis of results Evaluate supplier comments and tool functionality Compilation of scores Final Report (Completed December 2003) => recommendation to select ACTR

9 - ACTR - the selected tool Automated Coding by Text Recognition Developed by Statistics Canada Used by Lockheed Martin for the Census 2001 Processing System Automatic and interactive coding Consists of coding engine and maintenance tools; customer builds and tunes the coding index Generic: Can code a range of classifications Flexible: Allows different coding strategies, thresholds Has API and has been ported to UNIX/Windows Multiple coding databases Dependent coding using filters Powerful parsing capabilities

10 - Parsing Manipulation of text using global rules –Normalise, or reduce variation in text –Tune coding application Examples: –Replace/delete string –Replace/delete word, (synonym list) –Delete clause Applied to both reference files (i.e. coding index) and input files. Parsing data + coding index = Knowledge base

11 - ACTR matching algorithm Matching always follows parsing. Step 1: Find direct matches and assign codes Step 2: Find indirect matches (using Hellerman algorithm) –match scores based on word frequencies across index –unmatched words ignored (although more unmatched words lowers the score) –no fuzzy matching (except through parsing rules) Step 3: Assign codes based on user defined match parameters.

12 - Building knowledge base for SOC 2000 Based on SOC 2000 index Obtain test/tuning data (Census 1991 recoded descriptions) Development of parsing strategy Iterative development Index partitioned into 2 contexts –Main index entries –Default index

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18 - ACTR shortcomings Non-linguistic, ignores word order (e.g. Clerk to the Council is not equivalent to Council Clerk) No fuzzy matching (although particular cases of missing spaces and misspellings can be handled through parsing) Longer text strings difficult to code automatically No classifications mapping facility

19 - Next steps? Short term: Building knowledge bases Medium term: Implementing ACTR in individual business areas: –ASHE (Earnings) for coding occupation in April 2005 –IDBR (Industry) Medium/Long term: Operationalising ACTR in the new ONS environment, including CORD etc.

20 - The End

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