DLM-derived semantic-support measures #
@cite{baayen-2019} @cite{gahl-baayen-2024} @cite{saito-tomaschek-baayen-2025} @cite{heitmeier-chuang-baayen-2026}
Sibling to Defs.lean and Normed.lean. Hosts the family of semantic
support measures derived from a trained LinearDiscriminativeLexicon —
the per-coordinate, per-word, and per-region projections of
production that consumer studies have come to rely on.
What is in this file #
semSup D s j— semantic support for coordinatejof the form vector predicted from meaning vectors. Direct projection ofD.production sat indexj.semSupWord D s js— sum ofsemSupover a list of form coordinates. The natural "how strongly does the meaning predict this word's combination of form units" measure (paper §3.1 of @cite{saito-tomaschek-baayen-2025}; paper §3.4 of @cite{gahl-baayen-2024}).semSup_add/semSup_smul/semSup_zero—@[simp]linearity lemmas in the meaning argument; derived fromLinearMap.map_add/map_smul/map_zeroonD.production.
Carriers #
Specialised to FormVec n / MeaningVec d (i.e. Fin n → ℝ /
Fin d → ℝ), the most common DLM carrier types. All current consumers
(ChuangEtAl2026, LuChuangBaayen2026, Saito2025, GahlBaayen2024)
use these. A Measures/EuclideanSpace.lean sibling can host
inner-product-typed variants if a future consumer needs cosine
similarities directly.
Graduation history #
These measures lived inline in Phenomena/Phonology/Studies/Saito2025.lean
as the only consumer at first landing (CHANGELOG 0.231.17). Per
CLAUDE.md's ≥-2-consumers graduation rule, they lifted to substrate
when @cite{gahl-baayen-2024} landed as the second paper-anchored
consumer (CHANGELOG 0.231.18). The cross-paper concept of
"semantic support for form" is general DLM theory, not paper-specific.
Semantic support for coordinate j of the form vector predicted
from meaning vector s: semSup D s j = D.production s j.
The substrate's D.production s j already provides this directly;
the named binding makes the cross-paper concept (paper §3.1 eq. 1
of @cite{saito-tomaschek-baayen-2025}; the per-triphone support
of @cite{gahl-baayen-2024} §3.4) grep-able.
Equations
- Theories.Processing.Lexical.Discriminative.semSup D s j = D.production s j
Instances For
Word-level semantic support — the sum of semSup over a word's
component form coordinates. In the consumer studies' triphone-
based form representation, this is the sum over the word's
component triphones (paper §3.4 of @cite{gahl-baayen-2024} terms
this Semantic Support for Form; paper §3.1 eq. 2 of
@cite{saito-tomaschek-baayen-2025} terms it SemSupWord).
Equations
- Theories.Processing.Lexical.Discriminative.semSupWord D s js = (List.map (Theories.Processing.Lexical.Discriminative.semSup D s) js).sum
Instances For
semSup is linear in the meaning vector #
Since D.production is a LinearMap, semSup D · j is a linear
functional on the meaning space. Three structural lemmas (additivity,
scalar-multiplication, zero) follow by map_add / map_smul /
map_zero on the production map. @[simp] so consumers can rewrite
linear-combination meaning vectors directly.
semSupWord zero case #
semSupWord D 0 js = 0 follows from semSup_zero plus List.sum
of the constant-zero list. The general semSupWord_add /
semSupWord_smul linearity (sum of linear functionals is linear)
is deferred until a consumer needs to rewrite linear-combination
meaning vectors at the word level — induction on js plus ring
at the cons step works once Mathlib.Tactic.Ring is imported, but
no current consumer requires it.