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Automated Lexical Data Conversion Framework Development

digiteum.com

Information technology

Education

Other industries

Challenges in Lexical Data Conversion

Existing manual and fragmented dictionary conversion processes resulted in high error rates (up to 20%), inconsistent data quality, slow turnaround times (3 weeks-3 months per dataset), and scalability limitations due to incompatible tools and lack of automation across multilingual data sources.

About the Client

Global leader in human language technology and provider of lexical data for academic, technological, and business applications

Core Project Goals

Develop automated conveyor-based workflow for dictionary conversion
Achieve 99% data accuracy through standardized processing
Reduce conversion time by 10x while maintaining quality
Create flexible framework adaptable to multiple source/target formats

System Functional Requirements

Multi-format data pipeline (XML/PDF/semi-structured inputs)
Language-agnostic conversion engine
Automated error detection and correction
Customizable workflow configuration interface
Integration with existing QA and testing frameworks

Technology Stack

.NET

Python

ANTLR

Neo4j

Visual Studio

MSBuild

System Integrations

Existing Oxford language databases
Third-party format specification APIs
Customer-specific data delivery platforms

Non-Functional Requirements

Horizontal scalability for 300+ concurrent language projects
99.9% system uptime SLA
Data processing throughput of 10M+ entries/day
Role-based access control with GDPR compliance

Expected Business Impact

Enable production of 300+ dictionaries across 8 years with 10x faster turnaround, support licensing to Fortune 500 clients (Amazon/Google/Microsoft), and establish foundation for NLP/Machine Learning applications through standardized high-quality lexical datasets.