12 docs tagged with "Project motivation"

Mailwoman started as the answer to a record-matching problem, not a parsing one.

Users do not type addresses the way gazetteers store them. They type what they know, in the order they think of it, with the spellings their keyboard supports, trusting autocomplete suggestions they didn't verify. A parser that only handles well-formed addresses fails on real input.

Before you can judge a parser, you need to know what it is parsing for. Address parsing exists to route physical objects to physical places, through a system designed in the 19th century and patched ever since.

What kind of computation does mailwoman's neural parser do? When it looks at

A geocoder that correctly parses 90% of addresses sounds good. It sounds especially good when the alternative — building or buying a better one — costs engineering time or API fees. The trap is that the 10% tail is not random. It is concentrated in the populations you most need to serve: rural areas, developing economies, multifamily housing, and anyone whose address doesn't look like a suburban US single-family home.

Not every geocoding problem needs a neural parser. This series steel-mans the reasonably defensible compromises that work for most applications — the architectures that ship in days, cover 90% of addresses, and cost nothing to maintain. Each article describes one approach, when it works, what it loses, and where Mailwoman fits (or doesn't).

There is a persistent belief in geocoding: "If we just had a database of all addresses, this problem would be trivial." The belief is wrong in three independent ways. Together, they make the database approach structurally inadequate.

Traditional address parsers split the input into tokens, classify each token independently, then try to reassemble the pieces into a coherent parse. This sequence contains a structural circularity: you cannot group tokens correctly without knowing their types, and you cannot type them correctly without knowing their groups. The traditional architecture resolves this with heuristics, exceptions, and solver post-processing. The exception pile grows without bound.

If you've tried to describe Mailwoman to someone and reached for "it's like

The first five articles in this track established the problem:

Google's Geocoding API is the default choice for geocoding. It is fast, globally comprehensive, and returns results in milliseconds. For many applications, calling Google is the right move. For applications that need to own their address data, audit their results, or operate at scale, it is the expensive kind of cheap.

If Google's API is the proprietary default, geocode.earth is the open-source default. It runs Pelias — the geocoder Mailwoman forked — as a hosted service with global coverage, open data, and transparent pricing. For many applications, geocode.earth is the right answer. For applications that need to own their parser, it has the same fundamental limitation as every hosted API: you do not own the parsing layer.