Heitmeier, Chuang and Baayen (2026): The Discriminative Lexicon #
This file formalizes the relation between linearity and regularity in [heitmeier-chuang-baayen-2026]
(§16.6). The book's linear production maps handle inflectional systems with irregular forms:
trained on empirical embeddings, its English past-tense model produces 94% of the regular
and 74% of the irregular training verbs (§12.9). Meanings can instead be constructed, the
meaning of walked as the meaning of walk plus a past-tense vector (Table 12.7;
conceptualization is vector addition, (5.3), §16.3), which regularizes a system into "a
reasonable approximation of a truly regular system, and for such a system, linear mappings
appear to work quite well" (§16.6, Table 16.2); but with the meanings of held-out verbs
reconstructed this way, "none of the irregular verbs was produced correctly" (§12.9). The
file states why, in word-and-paradigm terms (Morphology.IsAnalogicallyRegular), on the
Morphology.Realization interface: over imputed semantics, a lexeme vector plus an
inflectional-function vector (imputed, the "imputed embeddings for stems and exponents" of
§16.6), a linear map's form table satisfies proportional analogy by construction, so a table
violating analogy is the paradigm of no linear DLM (not_exists_paradigm_eq_of_not_regular)
and forces positive training loss (pos_weightedLoss_of_not_regular), and when the lexeme
and function vectors are jointly independent a table is the paradigm of some linear DLM iff
it is analogically regular (exists_paradigm_imputed_eq_iff). Over word-specific
embeddings, by contrast, any table is the paradigm of some linear DLM as soon as the
embeddings are linearly independent (exists_paradigm_eq_of_linearIndependent), which
fills the irregular-linear cell of Table 16.2. The English past tense at the book's
letter-trigram coding (§12.9) is the irregular table: walk, walked against go, went.
References #
- [heitmeier-chuang-baayen-2026]
Imputed semantics #
A table violating proportional analogy is the paradigm of no linear DLM over imputed semantics: irregular forms cannot be produced from constructed meanings (§12.9).
Interpolation #
A linearly independent family of meanings can be sent anywhere by a linear map.
Over word-specific, linearly independent embeddings any form table is the paradigm of some linear DLM, irregulars included: the irregular-linear cell of Table 16.2, the English past tense on empirical embeddings (§12.9).
When the lexeme and inflectional-function vectors are jointly linearly independent, a table is the paradigm of some linear DLM over imputed semantics iff it is analogically regular: for a regularized system, linearity and regularity are the same constraint (§16.6).
The English past tense #
Equations
- HeitmeierChuangBaayen2026.instDecidableEqLetter x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
A regular and a suppletive verb.
Instances For
The base and past cells.
Instances For
The orthographic forms.
Equations
- One or more equations did not get rendered due to their size.
- HeitmeierChuangBaayen2026.form HeitmeierChuangBaayen2026.Verb.go HeitmeierChuangBaayen2026.Tense.base = [HeitmeierChuangBaayen2026.Letter.g, HeitmeierChuangBaayen2026.Letter.o]
Instances For
The letter-trigram cue inventory of the four forms (§12.9 codes forms as letter trigrams).
Equations
- One or more equations did not get rendered due to their size.
Instances For
The past-tense form table at the trigram coding.
Equations
Instances For
The English past tense violates proportional analogy: walked adds the cue ed# to walk,
went adds nothing to go. So no linear DLM produces it from constructed meanings
(not_exists_paradigm_eq_of_not_regular), while independent embeddings realise it
(exists_paradigm_eq_of_linearIndependent), as in §12.9.
Training on a paradigm #
The paradigm as a training experience: imputed semantics as the semantic matrix, the form table as the form matrix.
Equations
- One or more equations did not get rendered due to their size.
Instances For
Irregularity forces positive training loss: no mapping matrix fits a suppletive paradigm exactly, so every trained matrix carries residual error.