Consumer Products — Named Entity Recognition for Document Indexing

Overview

The primary purpose of this DAG is to utilize Named Entity Recognition (NER) techniques to classify and index documents sourced from diverse origins, such as internal reports, market research, and product specifications. The ingestion pipeline begins with the collection of these documents, which are then processed to extract relevant entities such as product names, categories, and other key identifiers. The processing steps include data cleansing, entity extraction, and validation to ensure qual

The primary purpose of this DAG is to utilize Named Entity Recognition (NER) techniques to classify and index documents sourced from diverse origins, such as internal reports, market research, and product specifications. The ingestion pipeline begins with the collection of these documents, which are then processed to extract relevant entities such as product names, categories, and other key identifiers. The processing steps include data cleansing, entity extraction, and validation to ensure quality and compliance with industry standards. Quality controls are implemented throughout the pipeline to monitor the accuracy of the extracted entities and to flag any anomalies or errors that arise during processing. The outputs of this DAG consist of structured data sets that are integrated into a content management system, thereby facilitating efficient search and access to critical information. Key performance indicators (KPIs) such as extraction accuracy, processing time, and error rates are monitored to evaluate the effectiveness of the workflow. The business value of this DAG lies in its ability to streamline document indexing, improve information retrieval times, and enhance decision-making processes within the consumer products industry.

Part of the Data & Model Catalog solution for the Consumer Products industry.

Use cases

• Improves efficiency in document indexing and retrieval
• Enhances data accuracy for informed decision-making
• Reduces manual effort in document classification tasks
• Facilitates compliance with industry standards
• Increases accessibility of critical product information