Entity Extraction

Identify the domain (e.g., medical, legal, e-commerce) and list the entity types you need to extract (e.g., person, organization, date, product). For each entity type, define attributes, aliases, and acceptable formats. This step prevents ambiguity downstream and sets clear extraction targets.

Why Notion AI 3.0: Notion AI 3.0 provides a flexible text editor and schema design environment suitable for defining entity schemas and scope.

Collect a representative sample of raw text (at least 50-100 documents) and manually annotate entities according to your schema. Use a labeling tool to create a gold-standard dataset. This annotated set will be used to train or fine-tune your extraction model and to evaluate accuracy.

Why Prodigy: Prodigy is a dedicated annotation tool for Named Entity Recognition, ideal for preparing and annotating sample data.

Choose between rule-based (regex, dictionaries), ML-based (NER model), or hybrid approach based on your schema complexity and data volume. For ML, select a pre-trained model (e.g., spaCy, Hugging Face) or plan to fine-tune. Configure the extraction pipeline with your schema and sample data.

Why spaCy: spaCy is a leading NLP library with built-in Named Entity Recognition, suitable for configuring extraction methods.

Execute the extraction pipeline on your sample data (or a held-out test set). Review the extracted entities against the gold-standard annotations to measure precision, recall, and F1 score. Identify common errors (e.g., missed entities, false positives) and refine your schema or model accordingly.

Why Parea AI: Parea AI provides experiment tracking and evaluation capabilities, useful for validating extraction output.

Based on error analysis, adjust your schema (add new entity types or attributes), update rules, or fine-tune the ML model with additional annotated examples. Re-run extraction and validation until accuracy meets your business threshold (e.g., >90% F1). This step may be repeated multiple times.

Why Prodigy: Prodigy supports iterative annotation and model refinement, essential for optimizing extraction.

Package the final extraction pipeline into a reusable service (e.g., API endpoint, batch processor, or library). Integrate with your application or data pipeline (e.g., load extracted entities into a database, CRM, or analytics tool). Monitor performance in production and set up logging for ongoing quality checks.

Why Zapier: Zapier enables workflow automation and integration, suitable for deploying and connecting extraction pipelines.

Create clear documentation covering the schema, pipeline configuration, accuracy metrics, and integration instructions. Include a quick-start guide for new users and a troubleshooting section for common issues. Hand over to the operations team or end users with a demo.

Why Notion AI 3.0: Notion AI 3.0 can generate meeting notes and documentation, suitable for documenting and handing over the pipeline.