Development of a Scalable Cloud-Based Corpus Building and Data Annotation Platform

The client faces significant limitations in their current corpus building system, which harvests and stores language data but struggles with performance, scalability, and extensibility when processing increasing volumes of raw text data. They need a system capable of ingesting vast amounts of data efficiently, with the ability to expand functionalities and incorporate additional data sources and linguistic analyses.

A leading academic or research organization specializing in Human Language Technology, aiming to build and process large-scale linguistic corpora for NLP and speech applications.

• Design and develop a cloud-based platform capable of harvesting up to 10x more data than existing systems, reaching processing volumes of over 8 million documents per day.
• Achieve processing times of approximately 3 hours for daily news feeds containing thousands of documents.
• Ensure the system can compile a balanced, annotated corpus of roughly 800,000 to 900,000 documents monthly, with an annual data volume exceeding 12 petabytes.
• Create a modular architecture allowing seamless addition of features such as sentiment analysis, genre recognition, and support for multiple data sources and languages.
• Implement elastic scalability and high reliability to support continuous data ingestion and processing without performance degradation.

• Automated data harvesting from multiple sources including news aggregators and social media APIs.
• Data filtering and quality assurance modules to ensure relevance and accuracy.
• Metadata enrichment and attribute augmentation for enhanced data analysis.
• Deduplication system to eliminate redundant entries at scale.
• Linguistic annotation tools leveraging industry-standard NLP toolkits (e.g., Stanford CoreNLP, OpenNLP).
• Secure, scalable storage solution utilizing cloud-native databases (e.g., document-oriented databases).
• Export capabilities to external corpus analysis and NLP tools such as sketch engines and custom analytics dashboards.
• An extensible modular architecture supporting future feature integrations.

• Multiple external data sources such as news aggregators and social media APIs
• External NLP and annotation tools
• Data export integrations with scholarly corpus tools and analysis platforms

• Ability to process up to 8 million documents daily with a processing time of approximately 3 hours
• Elastic scalability to handle 10x current data volumes without performance loss
• System high availability and fault tolerance
• Data security and compliance with relevant data privacy standards

The new platform aims to significantly enhance data harvesting and processing efficiency, enabling the client to manage and analyze vastly larger language corpora. Expected results include processing over 8 million documents per day within 3 hours, compiling up to 900,000 documents monthly, and handling annual data volumes exceeding 12 petabytes. These improvements will facilitate advanced linguistic research, improve NLP model training, and support extensive language technology development.