High Tech — Named Entity Recognition Extraction Pipeline

Overview

The Named Entity Recognition (NER) Extraction Pipeline is designed to streamline the process of extracting named entities from documents stored within a content management system. The primary data sources include PDF and DOCX files, which are commonly utilized in the high-tech industry for research and documentation. The ingestion pipeline begins with the collection of these documents, followed by a preprocessing step that prepares the data for analysis. This involves converting documents into a

The Named Entity Recognition (NER) Extraction Pipeline is designed to streamline the process of extracting named entities from documents stored within a content management system. The primary data sources include PDF and DOCX files, which are commonly utilized in the high-tech industry for research and documentation. The ingestion pipeline begins with the collection of these documents, followed by a preprocessing step that prepares the data for analysis. This involves converting documents into a uniform format, removing irrelevant content, and tokenizing the text for further processing. Next, advanced NER models are applied to identify and classify entities such as organizations, locations, and technical terms relevant to the field. Quality control measures are implemented to validate the accuracy of the extracted entities, ensuring that only high-quality data is retained. The final results are stored in a centralized data warehouse, making them accessible for further analysis and reporting through a robust API. Key performance indicators (KPIs) such as extraction accuracy rates and processing times are monitored continuously to assess the effectiveness of the pipeline. This automated approach not only enhances the efficiency of literature reviews but also significantly reduces the time and resources required for manual data extraction, ultimately driving better decision-making and innovation in the high-tech sector.

Part of the Literature Review solution for the High Tech industry.

Use cases

• Increases efficiency in literature review processes
• Reduces manual effort and potential for human error
• Enhances data-driven decision-making capabilities
• Facilitates quick access to relevant research insights
• Supports compliance and reporting requirements with accurate data