Simple Licencing Agreement
Description
Simple Licencing Agreement document sample
Document Sample


ONS Classification Coding
Tools Project
Occupation Classification Workshop
RSS, London, 21 June 2004
Nigel Swier
-
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).
-
ONS formed in 1996
Central Statistics
Office
Office of
Population Office for National
Censuses and Statistics
Surveys (OPCS)
Employment
Department
-
Statistical Modernisation
Programme (SMP)
Inherited Infrastructure: ONS vision:
• Multiple databases Single repository (Oracle)
• Multiple development tools Java (J2EE)
• Proliferation of statistical Standard statistical tools and
tools and methods methods (e.g. coding tool)
• Poor metadata Corporate metadata system
• Paper-based dissemination Web-based dissemination
• Risky statistical systems Robust statistical systems
• £75 million to deliver SMP (2003-2006)
-
Statistical Value Chain
Data Collection Operations on Operations on Dissemination
Unit Data Aggregate Data
• Survey design • Editing • Time series
• Survey case • Imputation • Tabulation
management • Disclosure Control
• Coding
• Weighting
• Estimation
Common ONS Statistical Tools
Corporate ONS Repository for Data (CORD)
ONS Metadata Repository
-
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
-
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
-
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
-
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
-
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
-
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.
-
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
-
-
-
-
-
-
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
-
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.
-
The End
-
Get documents about "