Automating Information Extraction with Named Entity Recognition and LLMs
Our project aimed to automate the process of extracting information from various data sources, including textual documents and database tables, utilizing Named Entity Recognition (NER) alongside advanced Language Models (LLMs).
Starting Point
Recognizing the time-consuming nature of manually extracting information from disparate data sources, we embarked on a project to automate this process.
Objective
Our primary objective was a seamless automation solution that could extract relevant information from diverse data sources using NER, and enhance metadata completeness and quality through LLMs.
Added Value
By integrating NER and LLMs into our automation solution, we provided significant added value to our clients, enabling them to streamline their workflow, reduce manual effort, and improve the consistency and quality data across their data sources.
From challenges to solutions
Data Source Variability
Dealing with the diverse formats and structures of data sources, such as PDFs, which posed challenges for accurate NER extraction.
Model Fine-Tuning
Ensuring the NER model could be effectively fine-tuned to different domains to extract relevant information.
Language Model Integration
Integrating LLMs seamlessly into the workflow to generate coherent and informative summaries and technical descriptions aligned with the extracted information.
Robust Preprocessing Techniques
Implementing robust preprocessing methods to standardize and parse data from different sources, improving the accuracy of NER.
Customizable Fine-Tuning Pipeline
Developing a flexible fine-tuning pipeline for the NER models, allowing clients to adapt it to specific domain requirements.
Optimized LLM Integration
Utilizing LLMs to generate summaries and technical descriptions tailored to the extracted information, ensuring coherence and relevance in the generated content.
Technical deep dive
Dive deep into our work on Automated information extraction
