Design a NPL taging system (claude version)

user: please help me design a nlp label system. the system should have a frontend and a backend. 1. there are multiple documents 2. user can label words in document by tags 3. user can add tag 4. tag might have relationship 5. user can connect tag to form an relationship.


Here’s a outline of a system for NLP labeling with a frontend and backend:


  • Display documents for labeling
  • Allow selecting/highlighting words/phrases to label
  • Provide input to enter tags and create new tags
  • Allow connecting tags to show relationships
  • Save user inputs to send to backend


  • Store all documents
  • Store all tags
  • Store tagging data from users (what tags were used to label what parts of what documents)
  • Store tag relationships
  • Expose API for frontend to fetch documents, tags, and tagging data
  • Use data from API to train NLP models to predict tags and relationships

Some additional considerations:

  • Authentication/authorization for users
  • Version control tagging data and documents
  • Handle updating/correcting tagging data
  • Visualize/export tagging data and relationships for analysis
  • Scale to potentially large corpora of documents and many tags/relationships

Does this help outline the major pieces you would need to implement? Let me know if you would like more details or have other questions!

user: How to do Version control tagging data and documents

assistant: Some good practices for version controlling tagging data and documents in an NLP labeling system:

Tagging data:

  • Store each version of the tagging data (what was labeled and relationships)
  • Associate tagging data versions with the document versions they were labeling
  • Allow reverting to previous tagging data versions if needed
  • Note who made changes and when for each version


  • Store each version of documents
  • Allow uploading new document versions to replace old ones
  • Don’t lose previous document versions in case tagging data needs to revert to a previous version
  • Note who uploaded new document versions and when

This allows you to have an audit trail of changes and revert if needed. Some options for implementation include:

  • Saving all versions in a database with version numbers/timestamps
  • Committing changes to a Git repository to take advantage of version control features
  • A hybrid approach using a database but with periodic commits to Git

The right approach depends on the scale of data and complexity of version control needs. Version control is important for data quality and correcting mistakes, so incorporating it upfront is worthwhile.

user: How to do collaborate tagging between multiple user?


Some key things to consider for collaborative tagging between multiple users:

  • Allow tagging data to be merged from multiple users
  • Handle conflicts if two users label/relate the same things differently
  • Permissions to control which users can modify which tagging data
  • Communication between users about tagging disputes or ambiguities
  • Consistency in tagging across users through guidelines/training
  • Ability to review tagging data from specific users and identify low-quality data
  • Consider having multiple users validate tagging data to improve accuracy
  • If possible, assign different users to label overlapping sections of documents to cross-validate

Enabling collaboration makes it possible to scale NLP labeling to large datasets, but it does introduce additional complexity. With some thoughtful design around merging, permissions, consistency, and validation, you can build a system to support multi-user collaborative tagging.

me: the result seems pretty ok.