evidence retrieval
**Evidence retrieval** is the NLP task of finding **documents, passages, or data** that support or contradict a given claim. It is the second step in the fact-checking pipeline, connecting identified claims with the relevant information needed to verify them.
**How Evidence Retrieval Works**
- **Query Formulation**: Convert the claim into an effective search query. The claim "Global temperatures rose 1.5°C" might become a query for climate data, IPCC reports, or temperature records.
- **Document Retrieval**: Search large corpora (web, knowledge bases, scientific literature, fact-check archives) for relevant documents.
- **Passage Extraction**: Identify the specific paragraphs or sentences within retrieved documents that contain relevant evidence.
- **Relevance Ranking**: Rank retrieved evidence by relevance and reliability.
**Retrieval Approaches**
- **Sparse Retrieval (BM25/TF-IDF)**: Traditional keyword-based search. Fast and effective for claims with distinctive terms.
- **Dense Retrieval**: Use neural encoders (BERT, Contriever, E5) to embed claims and documents in the same vector space, finding semantically similar evidence even without keyword overlap.
- **Hybrid (Dense + Sparse)**: Combine keyword and semantic search using **Reciprocal Rank Fusion (RRF)** for better recall.
- **Knowledge Graph Lookup**: For claims about entities and relationships, query structured knowledge bases (Wikidata, DBpedia) directly.
- **Web Search**: Use search engines to find relevant web pages, especially for recent or niche claims.
**Evidence Sources**
- **Wikipedia**: Massive, structured, and frequently updated — the primary evidence source for many fact-checking systems.
- **Scientific Literature**: PubMed, Semantic Scholar for health and science claims.
- **Government Data**: Census data, economic statistics, public health records.
- **Fact-Check Archives**: Previously checked claims from Snopes, PolitiFact, Full Fact.
- **News Archives**: Verified news reports from reputable sources.
**Challenges**
- **Source Reliability**: Not all retrieved evidence is trustworthy — misinformation appears in search results too.
- **Temporal Relevance**: Claims about "current" statistics need up-to-date evidence, not outdated snapshots.
- **Multi-Hop Reasoning**: Some claims require combining evidence from multiple sources.
- **Stance Detection**: Determining whether retrieved evidence **supports or refutes** the claim adds complexity.
Evidence retrieval is the **backbone of automated fact-checking** — even the best verdict prediction model is useless without relevant, high-quality evidence to reason over.