Telecom — Named Entity Extraction from Client Documents

Overview

The purpose of this DAG is to streamline the extraction of named entities from client documents stored in object storage, significantly improving the efficiency of literature reviews in the telecom industry. The primary data sources include PDF files and Word documents containing relevant client information. The ingestion pipeline begins with the retrieval of these documents, followed by a series of processing steps that include text analysis to identify and extract named entities, normalization

The purpose of this DAG is to streamline the extraction of named entities from client documents stored in object storage, significantly improving the efficiency of literature reviews in the telecom industry. The primary data sources include PDF files and Word documents containing relevant client information. The ingestion pipeline begins with the retrieval of these documents, followed by a series of processing steps that include text analysis to identify and extract named entities, normalization of the extracted data to ensure consistency, and enrichment with additional metadata to enhance the context of the entities. Quality control measures are implemented at various stages to verify the accuracy of the extracted entities, ensuring that only high-quality data is processed. The results of this extraction process are then stored in a data warehouse, making them accessible through a dedicated search interface. Key performance indicators (KPIs) for monitoring the effectiveness of this DAG include the precision rate of extracted entities and the overall processing time, which are critical for evaluating the efficiency of the workflow. By automating the extraction process, this DAG delivers significant business value by reducing manual effort, improving data accuracy, and enabling faster access to critical client information, ultimately supporting better decision-making in the telecom sector.

Part of the Literature Review solution for the Telecom industry.

Use cases

• Reduces manual data extraction efforts significantly
• Enhances accuracy and consistency of client data
• Improves search capabilities for client information
• Speeds up literature review processes in telecom
• Facilitates better decision-making with reliable data