Automated Text-Based Event Filtering System Using AI and NLP

The client operates a geospatial platform aggregating event data from multiple sources, which often include irrelevant or out-of-scope entries such as indoor activities. Manual verification is becoming unsustainable as data volume grows, leading to challenges in maintaining content quality and relevance for outdoor activity-based applications.

A mid-sized digital mapping and geospatial data provider offering location-based services and user engagement platforms that require accurate event data filtering to ensure content relevance.

• Develop an AI-powered system capable of accurately classifying and filtering event data, targeting a classification accuracy of approximately 95%.
• Automate the process of selecting outdoor events and activities, reducing manual verification efforts and increasing scalability.
• Improve data quality and relevance for end-users by ensuring only pertinent outdoor events are displayed.
• Leverage limited training data effectively through transfer learning and pretrained NLP models.

• Event classification module utilizing pretrained natural language processing models (e.g., transformer-based architectures) for high-accuracy filtering.
• Transfer learning approach to optimize model performance with limited labeled data samples.
• Data ingestion interface to process streaming data from multiple external providers.
• Filtering mechanism to automatically select relevant outdoor events and discard indoor or irrelevant entries.
• Feedback loop for continuous model improvement based on user interactions and updated data.

• Streaming data sources from multiple event providers
• Mapping and geospatial data platforms for contextual relevance
• User feedback and reporting systems for model refinement

• Achieve approximately 95% classification accuracy in filtering outdoor events
• Ensure system scalability to handle increasing data volume streams
• Maintain low latency for real-time data processing and filtering
• Ensure data security and compliance with relevant privacy standards

The implementation of this AI-powered event verification system is expected to significantly reduce manual data review efforts, enable scalable processing of large event data streams, and improve the relevance of content for outdoor activity applications. This could lead to enhanced user engagement, increased accuracy of event listings, and a more efficient data management process.