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UID:pretalx-foss4g-uk-2026-KXZTSY@talks.osgeo.org
DTSTART;TZID=GMT:20261013T153000
DTEND;TZID=GMT:20261013T160000
DESCRIPTION:Postal address parsing has two well-worn approaches: hand-tuned
  rule engines that break down outside their home locale\, and large langua
 ge models too heavy to ship to a browser. Mailwoman is a compact neural se
 quence labeler fed by reference data (postal registries\, gazetteers\, pos
 tcode indexes) supplied to the model as soft evidence rather than hard ove
 rrides.\n\nThe result is a full geocoder. A shared ~40 MB int8 ONNX model 
 (SentencePiece tokenization\; BIO decoding with transition masking and Vit
 erbi) covers every locale\, and each additional locale ships as an overlay
  of a few hundred kilobytes. The same weights run under Node.js and in the
  browser over WebGPU or WASM\, next to a SQLite-WASM gazetteer\, so the en
 tire parse-then-geocode path can execute client-side. For existing tooling
 \, Mailwoman exposes drop-in Nominatim\, Photon\, and libpostal-compatible
  APIs.\n\nThis talk covers what we learned shipping it: how we ruled out t
 he rules-engine\, and how OpenStreetMap\, Overture\, Who's on First\, and 
 national open-address registers (BAN in France\, LINZ in New Zealand\, TIG
 ER in the US) feed the training and resolution pipeline. We close it off w
 ith the recipe for the en-GB build\, and how the same pipeline can be used
  to add any locale with a gazetteer and a labeled corpus.\n\nAttendees wil
 l leave able to evaluate whether their own address pipeline could benefit 
 from a neural parser\, and with a concrete recipe for adding a new locale.
DTSTAMP:20260821T112139Z
LOCATION:Think
SUMMARY:Truth or Consequences\, New Mexico: Approximately Correct - Teffen 
 Ellis
URL:https://talks.osgeo.org/foss4g-uk-2026/talk/KXZTSY/
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