A production-ready 3.8B parameter language model optimized for zero-shot financial entity extraction. Validated on Indian banking syntax (HDFC, ICICI, SBI, Axis, Kotak) with 94.5% field accuracy.

For years, SEOs optimized pages around keywords. But Google now understands meaning through entities and how they relate to one another: people, products, concepts, and their topical connections ...

Knowledge graphs (KGs) have become a widely adopted standard for knowledge representation in the semantic web. Currently, significant efforts have been invested in constructing a KG with a primary ...

Abstract: Entity extraction and relational extraction are the essential parts of the information extraction task, with important theoretical significance and broad application prospects. In terms of ...

In this tutorial, we demonstrate how to construct an automated Knowledge Graph (KG) pipeline using LangGraph and NetworkX. The pipeline simulates a sequence of intelligent agents that collaboratively ...

contract-entity-extractor/ ├── src/ │ └── contract_entity_extractor/ │ ├── cli.py # CLI entry point (Typer) │ ├── core.py # Extraction logic │ ├── models.py # Data models │ ├── patterns.py # Regex ...

Abstract: As an important subtask of natural language processing, the purpose of entity relation extraction is to extract entity relation triples from unstructured text data. The existing cascade ...