7 docs tagged with "Rule + neural hybrid"

A short lineage for readers who already know Pelias.

The FST (finite-state transducer) gazetteer prior is the structure that lets the neural classifier benefit from everything Who's On First already knows. The neural model knows grammar; the gazetteer knows places. The FST is the bridge — pre-computed at build time so the classifier can consult it at inference time without paying gazetteer-lookup costs per token.

The neural classifier doesn't know what Boston is. It knows that the

This is a roadmap snapshot, current as of May 2026. For current state, see the scope declaration, How Mailwoman parses an address, How Mailwoman resolves a place, and How it works now.

Mailwoman runs addresses through a staged pipeline. Each stage adds one kind of knowledge the stages below it cannot easily derive. Two models are involved, not one: a small coarse-placer that guesses the country up front (Stage 1.5), and the main neural classifier that labels the tokens (Stage 3). Rule classifiers run alongside the neural classifier, and a policy registry decides whose vote wins for each address component. Outside knowledge — postcodes, the gazetteer — reaches the model as soft hints called anchors, which inform a decision but never override it.

This is the design reference for the work that fills the WOF hierarchy gap. It synthesizes the architectural decisions reached during the v0.6.1 postmortem and the subsequent design consult. Code in neural/, resolver-wof-sqlite/, and core/ refers back here; this article tells you why each piece looks the way it does.

The first five articles in this track established the problem: