---
title: "coref-lexicon — Mova code model"
description: "Mova code model of coref/src/lexicon.rs: components with their labels, obernants and the model text."
url: "https://groupants.elamur.ai/free/en/mova/coref/src/lexicon.rs.groupant/"
---
Elamur: [Mova](https://md.mova.elamur.ai/free/en/) · [Dictionary](https://md.dictionary.elamur.ai/free/en/) · [Research](https://md.research.elamur.ai/free/en/) · **Groupants**

This page as HTML: <https://groupants.elamur.ai/free/en/mova/coref/src/lexicon.rs.groupant/>

# coref-lexicon

The groupant beside coref/src/lexicon.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.

## Facts

- **kind:** groupant
- **status:** partial source bound model
- **source:** [`coref/src/lexicon.rs`](https://github.com/ElamurAI/mova-free/blob/main/coref/src/lexicon.rs)
- **source SHA-256:** `31cd78fe55f46926…`
- **repository folder:** [`coref/src/lexicon.rs.groupant`](https://github.com/ElamurAI/mova-free/tree/main/coref/src/lexicon.rs.groupant)
- **repository commit:** `48f2e6f74146`

> **Implementation material.** A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.

## Components

| component | label | where |
|---|---|---|
| **approximant** | partial | [approximant](#approximant) |
| **ast** | partial | `syntax.json` |
| **descriptant** | present | [descriptant](#descriptant) |
| **invariant** | partial | [invariant](#invariant) |
| **tests** | executed bounded checks | [tests](#tests) |

## Obernants

The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.

| id | type | label | where |
|---|---|---|---|
| `behavior_complement` | behavior_complement | partial source derived model | [behavior_complement](#behavior-complement) |
| `reverse_reconstruction` | reverse_reconstruction | partial source derived model | [reverse_reconstruction](#reverse-reconstruction) |
| `path_precision` | path_precision | partial source derived model | [path_precision](#path-precision) |
| `decision` | decision | partial source derived model | [decision](#decision) |
| `licence_scope` | licence_scope | partial source derived model | [licence_scope](#licence-scope) |

## Model text


### descriptant

#### coref-lexicon

A gender, number and animacy lexicon of common nouns, counted from Mova's own parses (`en annotate` with `en.bin`)
over high-precision noun–pronoun dependency paths after Bergsma and Lin 2006, smoothed into per-lemma shares; the
mention features it gives the coreference sieve (`mention.rs`) and the frame reader (`prag::frames::lex_table`).

State: one tree at a time while counting; `Counts` maps keyed by (lemma, source, path, class); a process-wide table
loaded once from `COREF_GENDER`, `$MOVA_DATA/coref/gender.tsv` or `~/mova-data/coref/gender.tsv`; thresholds from
`COREF_GENDER_TAU`, `COREF_GENDER_MIN`, `COREF_GENDER_ALPHA`.

Epoch 2026-11-04: first model, written with the code; the predictions below were stated before the corpus runs.

### approximant

#### Forward model

For a sentence s, `hits(s)` is the sorted list of (lemma, class, path) for every pronoun or relative word that a
path joins to a countable noun. For a table T with counts c and corpus prior π over masc/fem/neut,
p_k = (c_k + α π_k) / (c_masc + c_fem + c_neut + α) and p_anim = (c_masc + c_fem + c_who + α π_anim) /
(c_masc + c_fem + c_neut + c_who + c_which + α). `feats` returns ANIM when p_anim ≥ τ, INAN when 1 − p_anim ≥ τ
(each with at least `min_n` counts), gender MASC/FEM/NEUT when that share ≥ τ, MASC|FEM when the personal share is;
contradictory or empty results are None. Defaults τ = 0.8, min_n = 3, α = 2.

Prediction (2026-11-04, before the runs): CoNLL F1 on GUM dev within ±0.5 (GUM is mostly names and abstract nouns),
a gain on pronouns with person-noun antecedents; the rotated table lower than both.

### invariant

#### Invariant and cost

Counting is a pure function of the trees: block boundaries do not change counts (a block is a whole number of
sentences and documents do not carry state). The table text is a function of the merged counts (BTreeMap order).
`feats` never gives NEUT together with ANIM nor INAN together with MASC or FEM. A missing, `off` or refused table
gives the features of the build without the lexicon.

hits O(n²) per sentence; counting linear in words plus block parsing; build O(K log K); lookup O(log L).

### models

### behavior_complement

The lexicon never reads a proper noun (no gender from names), a noun in a compound, flat name or coordination, a
pronoun in another quote than the noun, a negated or prepositional predicate, or a plural noun with a singular
pronoun. It never decides a lemma seen fewer than `min_n` times, and never overrides gender or animacy that the tree,
the closed lists or a title already gave (mention.rs uses it only when gender is unknown). A CC BY-SA table is
refused unless `COREF_GENDER_INTERNAL=1`; the unit tests never read the data directory.

### reverse_reconstruction

From a row one recovers the per-class totals and, from the `paths`, `sources` and `example` columns, which paths and
sources contributed and one smallest example sentence id; not the other sentences. From a Feats value one recovers
only the set of rows clearing the same thresholds. From the rotated control table no lemma's own counts are
recoverable without the rotation offset.

### path_precision

Precision of each path against gold coreference of GUM dev (gold trees, measured 2026-11-04, `coref lexicon paths`):
poss 20 of 25, pred 22 of 25, refl 0 of 1, say 0 of 2; relpron is not measurable (GUM does not annotate relative
pronouns). Pairs over the corpus (13.87 million sentences): poss 336 169, pred 257 958, refl 62 016, say 33 005,
appos 2 360, relpron who 187 237 and which 302 652; the table header splits them by class.

### decision

The decision is a threshold on smoothed shares; between τ = 0.7 and 0.9 and min_n 3 to 30, GUM dev CoNLL F1 stays
within 0.1 (gold trees 75.22–75.29, SLM trees 70.61–70.69 with singletons).

### licence_scope

Sources: 5 110 public-domain (EU/UA) Project Gutenberg books of `corpora/tales/free` and the school's lessons (ours).
Parsed by `en.bin` (CC BY-SA 4.0 model). The table lives outside the repository; whether a count table derived
through a CC BY-SA model may ship follows the open exception of `licence/DATA.md`. The Wikipedia table is CC BY-SA,
internal only.

### tests

#### Evidence — 2026-11-04

`cargo test --release -p coref`: 25 library tests pass, including the four lexicon tests. Negative controls inside
them: negation, prepositional predicate, a name, a plural noun with «himself» and a possessive inside the subject give
no pair; `lexicon_feature_keeps_it_from_a_person_noun` shows «it» → farmer without the table and → stone with it.

Measured 2026-11-04 (dev only, CPU; runs in `$MOVA_DATA/runs/coref-gender-2026-11-04`). The prediction held: GUM dev
CoNLL F1 without singletons 64.36 → 64.29 (gold trees) and 58.81 → 58.87 (SLM trees); third-person pronouns right
498 → 500 and 470 → 478; rotated table 63.74 / 58.33 and 481 / 454 (red). Frame bench dev coref F1 0.471 → 0.489,
rotated 0.466. School with the reader's opt-in: 24 courses 12 306 → 12 305, books 7 339 → 7 339, rotated 12 285 and
7 283; so the reader keeps it opt-in. Switch off = main byte for byte (GUM eval, frame bench, four courses with the
opt-in build); two runs with the table identical; `lexicon_feature_keeps_it_from_a_person_noun` goes red with
`COREF_X=no-gender-table`.