beita6969 Information Extraction
Purpose
Extract structured information (entities, relations, events, clauses) from unstructured scientific and domain-specific text.
Key Datasets
- ChemProt (bigbio/chemprot): Chemical-protein interaction extraction from BioCreative VI; 10 relation types (CPR:3-CPR:9) between chemicals and proteins
- CUAD (atticus-project/cuad): Contract Understanding Atticus Dataset; 41 clause types from 510 legal contracts (CC-BY licensed)
- JNLPBA: Biomedical named entity recognition (protein, DNA, RNA, cell line, cell type)
- SciERC: Scientific entity and relation extraction from AI paper abstracts
Protocol
- Schema definition — Define target entity types, relation types, and attributes
- Preprocessing — Sentence segmentation, tokenization, abbreviation expansion
- Entity recognition — Identify and classify named entities (NER)
- Relation extraction — Detect relationships between entity pairs (RE)
- Normalization — Map entities to standard ontologies (MeSH, ChEBI, UniProt)
- Output structuring — Format as structured JSON, RDF triples, or knowledge graph
Extraction Types
- Chemical-protein interactions: Substrate, inhibitor, agonist, antagonist, activator
- Legal clause extraction: Termination, IP rights, non-compete, indemnification, limitation of liability
- Gene-disease associations: Causal, biomarker, therapeutic target
- Drug-drug interactions: Synergistic, antagonistic, pharmacokinetic
Rules
- Report extraction confidence scores for each entity/relation
- Provide span offsets for traceability back to source text
- Normalize entities to standard identifiers (CAS, UniProt ID, etc.)
- Handle nested entities and overlapping relations
- Validate extracted facts against known databases when possible