feat(text): add meta.* corpus-footprint layer and 4 language-aware primitives (v0.1.3)
Adds 12 new primitives across two waves of spec work this session.
meta.* layer (8 primitives) — corpus-snapshot footprint:
total_messages, corpus_span_days, msg_per_day, active_days,
activity_density, first_seen_ts, last_seen_ts, fingerprint_confidence.
Motivated by two actors with identical message counts (53 each) producing
indistinguishable profiles despite radically different presence shapes
(0.3-day burst vs 47-day long tail).
Language-aware characterization primitives (4 primitives):
stylometric.pos_ngram_signature — SimHash over POS bigram frequency vector;
syntactic skeleton fingerprint that survives full vocabulary paraphrase.
lexical.dialect_region — BCP-47 free_string (es-CL, es-AR, es-MX, …);
designed for EYENET integration with INGEOTEC regional-spanish-models.
lexical.evaluative_morphology_density — diminutive/augmentative/pejorative
suffix density; stable per-author trait baked into language acquisition.
lexical.optional_grammar_signature — SimHash over optional-grammar choice
points (compound/simple past, subjunctive, leísmo, relative pronoun);
high-reliability Spain vs LatAm discriminator.
Also fixes stale scratchpad.md references throughout (README.md is now the
authority), bumps behave-text to 0.1.3, and updates CHANGELOG.
This commit is contained in:
@@ -51,7 +51,7 @@ topic = event_topic_for("stylometric.capitalization_habit")
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| `Observation` | Registry-aware subclass of `behave_core.spec.Observation`. Validates `primitive` and `value` against `PRIMITIVE_REGISTRY`. |
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| `Window` | Re-exported from `behave_core`. |
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| `ObservationValue` | Re-exported union type. |
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| `PRIMITIVE_REGISTRY` | `dict[str, ValueTypeSpec]` — the full primitive catalog (35 entries). |
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| `PRIMITIVE_REGISTRY` | `dict[str, ValueTypeSpec]` — the full primitive catalog (47 entries). |
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| `ValueKind` | Enum: `CATEGORICAL`, `NUMERIC`, `HASH`, `ARRAY`, `FREE_STRING`, `BOOL`. |
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| `ValueTypeSpec` | Pydantic model: kind, allowed values, bounds, notes. |
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| `is_known(primitive)` | `bool` — whether a primitive path is registered. |
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@@ -64,11 +64,33 @@ present in `behave-shell` but not yet implemented here — `status: planned`.
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## Primitives
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35 primitives across 6 categories.
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47 primitives across 7 categories.
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---
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### `stylometric.*` — Writing style fingerprints (12 primitives)
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### `meta.*` — Corpus-snapshot footprint (8 primitives)
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Meta primitives describe the actor's presence in the corpus window itself —
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how many messages, how long a span, how densely distributed. They are not
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stylometric features; they are the scaffolding that other primitives assume.
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Several primitives (notably `temporal_evolution.lifecycle_phase`) implicitly
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depend on these quantities; `meta.*` makes them first-class so downstream
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attribution engines can access and weight them explicitly.
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| Primitive | Kind | Description |
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|---|---|---|
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| `meta.total_messages` | numeric | Raw message count for this actor in the corpus snapshot. Anchor for `msg_per_day` and `fingerprint_confidence`. |
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| `meta.corpus_span_days` | numeric | Wall-clock fractional days between first and last message. First-to-last only — blind to gaps. A 47-day span with 5 active days still yields 47. Recomputable from `first_seen_ts` / `last_seen_ts`. |
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| `meta.msg_per_day` | numeric | `total_messages / corpus_span_days`. Separates bursty visitors (53 msgs / 0.3 days = 53/day) from long-tail lurkers (53 msgs / 47 days = 1.1/day). Undefined when span = 0; extractors emit null/omit rather than divide-by-zero. |
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| `meta.active_days` | numeric | Distinct calendar days (UTC) with ≥1 message. Always ≤ `corpus_span_days`. Distinguishes a periodic visitor (span=47, active=3) from a near-daily regular (span=47, active=40). |
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| `meta.activity_density` | numeric [0,1] | `active_days / corpus_span_days`. 1.0 = present every day of the window. Near-0 = appeared once or twice across a long window. Undefined when span = 0; emit null/omit for single-day actors. |
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| `meta.first_seen_ts` | free_string | ISO 8601 timestamp (UTC offset) of the actor's earliest message. Anchors `corpus_span_days` in absolute time for cross-extraction comparison. |
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| `meta.last_seen_ts` | free_string | ISO 8601 timestamp (UTC offset) of the actor's latest message. See `first_seen_ts`. |
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| `meta.fingerprint_confidence` | categorical | Qualitative reliability of this actor's full fingerprint: `low`, `medium`, `high`. Attribution engines should weight all other observations by this before compositing. Derivation is **extractor-defined** — extractors declare their heuristic in the source label (e.g. `#confidence-v1`). |
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---
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### `stylometric.*` — Writing style fingerprints (13 primitives)
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Stylometric primitives capture the unconscious writing habits that distinguish
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one author from another. The field goes back to the Mosteller-Wallace Federalist
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@@ -92,10 +114,11 @@ the Rutify corpus are noted inline where they affect interpretation.
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| `stylometric.function_word_distribution_top200` | hash | 64-bit SimHash over the 200 most common Spanish function words. The wider list reaches into the long tail (rare-but-individual words like `tampoco`, `aunque`, `mientras`) that carry more discriminating signal in short-message corpora. Not yet emitted by v0 prototype — populated in v0.2. |
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| `stylometric.character_ngram_simhash` | hash | 64-bit SimHash over character n-gram frequencies (default n=3), lowercased. Orthogonal to function-word distributions: captures punctuation tics, accent-stripping habits, typo patterns, and idiom fragments that survive paraphrase. Accents are preserved because accent-stripping is itself a stylistic tic. Source label declares n size (e.g. `#char3gram`). |
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| `stylometric.distinctive_vocabulary_signature` | hash | 64-bit SimHash over a TF-IDF-weighted top-K rare-word vector. Captures the author's distinctive lexicon — words they use that other authors in the same corpus do not. Complementary to function-word distributions: where `function_word_*` captures common-word style, this captures individual lexical choice. Requires the full corpus for IDF computation. Source label declares top-K and corpus tag (e.g. `#tfidf-top50`). |
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| `stylometric.pos_ngram_signature` | hash | 64-bit SimHash over a POS n-gram (default bigram) frequency vector. Captures syntactic skeleton independent of vocabulary — an author can change every word and retain the same grammatical fingerprint. Orthogonal to character n-grams and function-word distributions. Tagger-dependent: source label must declare tagger, language model, and n (e.g. `#spacy-es_core_news_sm-bi`). Calibration note: chat-domain text produces tagger noise — weight low until validated on labelled chat corpora. |
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---
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### `lexical.*` — Vocabulary and linguistic patterns (8 primitives)
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### `lexical.*` — Vocabulary and linguistic patterns (11 primitives)
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Lexical primitives characterize *what* and *how* an actor writes at the word and
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sentence level. Where stylometric primitives fingerprint unconscious micro-habits,
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@@ -112,6 +135,9 @@ how questions are formed, register.
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| `lexical.sentence_complexity_class` | categorical | Dominant clause structure. `simple` = single-clause. `compound` = two independent clauses joined by coordinating conjunctions (pero, y, o). `complex` = dependent clauses and subordination (aunque, porque, cuando). Reflects education level and cognitive investment. |
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| `lexical.question_formation_style` | categorical | How questions are formed. `punctuation_only` = question mark without interrogative words ('¿Cuánto?') — very common in Spanish chat. `lexical` = explicit interrogatives (¿qué, cómo, cuándo). `formal` = inverted subject-verb or formal register. |
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| `lexical.imperative_style` | categorical | How commands and requests are framed. `informal_directive` = tú/vos imperative (dame, hazlo). `formal_directive` = usted imperative (hágame el favor). `polite` = conditional/modal softening (¿podría...?). Stable per-author trait in hierarchical contexts. |
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| `lexical.dialect_region` | free_string | Dominant regional variety of the actor's matrix language as a BCP-47 language-region tag (e.g. `es-CL`, `es-AR`, `es-MX`, `es-ES`, `en-US`). Detected from lexical marker density against per-region vocabulary tables. Emit literal `unknown` below confidence threshold. Detection method declared in source label (e.g. `#dialect-markers-v1`). Complementary to `code_switching_matrix_language`, which derives language via switching analysis rather than direct marker lookup. |
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| `lexical.evaluative_morphology_density` | numeric [0,1] | Rate of evaluative morpheme tokens / total tokens. Covers Spanish diminutives (`-ito`/`-ita`), augmentatives (`-ón`/`-ote`), pejoratives (`-ejo`/`-ucho`), and intensives (`-azo`). Heavy diminutive use is characteristic of Mexican/Central American Spanish; River Plate speakers use them significantly less. Stable per-author — baked into language acquisition and hard to consciously suppress. Source label declares morpheme set and tool version (e.g. `#eval-morph-es-v1`). |
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| `lexical.optional_grammar_signature` | hash | 64-bit SimHash over the author's preference probability vector at optional-grammar choice points. For Spanish: compound vs simple past (`he comido` vs `comí` — high-reliability Spain/LatAm discriminator), subjunctive usage rate, leísmo/laísmo/loísmo clitic patterns, and relative pronoun choice (`que` vs `el cual`). Each choice point is a scalar [0,1]; the SimHash is computed over the concatenated vector. Choice-point set is extractor-defined and declared in source label (e.g. `#optgrammar-es-v1`). Requires sufficient corpus volume for stable probabilities — gate on `meta.fingerprint_confidence` before use. |
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---
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@@ -2,7 +2,7 @@
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# BEHAVE-TEXT Attribution Recipes
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> **This document is not part of BEHAVE-TEXT.** BEHAVE-TEXT (`scratchpad.md`) defines the observation taxonomy and emission envelope. It does **not** assert who an actor is, link sessions, or assign profiles. Those are attribution-engine concerns.
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> **This document is not part of BEHAVE-TEXT.** BEHAVE-TEXT (`README.md`) defines the observation taxonomy and emission envelope. It does **not** assert who an actor is, link sessions, or assign profiles. Those are attribution-engine concerns.
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>
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> This document is a **placeholder**. Recipes for the text domain wait for corpus calibration. The Rutify Telegram corpus (forthcoming) will be the labeling ground truth that drives the first concrete profiles.
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@@ -16,7 +16,7 @@ PII discipline notice (carried over from behave-core's envelope module):
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IS text content. Sensors must hash/aggregate before emitting.
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Adding a new primitive is a deliberate registry edit. Drift between this file
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and `scratchpad.md` is a bug; v0 keeps the registry hand-written so PR review
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and `README.md` is a bug; v0 keeps the registry hand-written so PR review
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catches drift, v0.x may auto-extract from the markdown if drift becomes a
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maintenance issue.
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@@ -109,10 +109,71 @@ def _array(of: ValueKind, notes: Optional[str] = None) -> ValueTypeSpec:
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# ─── The registry ───────────────────────────────────────────────────────────
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#
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# 28 primitives across 4 layers. Mirrors scratchpad.md row-for-row.
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# 47 primitives across 7 layers. Mirrors README.md row-for-row.
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PRIMITIVE_REGISTRY: dict[str, ValueTypeSpec] = {
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# ── stylometric.* (motor analog — 8) ──────────────────────────────────
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# ── meta.* (corpus-snapshot footprint — 8) ────────────────────────────
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"meta.total_messages": _num(
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min_val=0.0,
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notes="Raw message count for this actor in the corpus snapshot. Integer in "
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"practice; stored as float for spec uniformity. Dependency anchor: "
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"msg_per_day is derived from this; fingerprint_confidence is informed "
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"by this. Emit before deriving rates.",
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),
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"meta.corpus_span_days": _num(
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min_val=0.0,
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notes="Wall-clock duration in fractional days between the actor's earliest "
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"and latest message in the corpus snapshot. First-to-last only — blind "
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"to silence in between (a 47-day span with 5 active days still yields "
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"47). Complement with active_days and activity_density to get presence "
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"shape. Recomputable from first_seen_ts and last_seen_ts.",
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),
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"meta.msg_per_day": _num(
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min_val=0.0,
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notes="total_messages / corpus_span_days. The key rate that separates a "
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"bursty single-session visitor (53 msgs in 0.3 days → 53/day) from a "
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"long-tail lurker (53 msgs in 47 days → 1.1/day). Undefined when "
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"corpus_span_days = 0; extractors should emit null/omit rather than "
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"divide-by-zero in that edge case.",
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),
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"meta.active_days": _num(
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min_val=0.0,
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notes="Count of distinct calendar days (UTC) on which the actor sent at "
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"least one message. Always ≤ corpus_span_days. An actor with span=47 "
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"and active_days=3 is a periodic visitor who appears rarely; one with "
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"span=47 and active_days=40 is a near-daily regular. Use alongside "
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"activity_density for full presence shape.",
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),
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"meta.activity_density": _num(
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min_val=0.0, max_val=1.0,
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notes="active_days / corpus_span_days. Single scalar capturing 'how filled "
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"is the span?'. 1.0 = present every day of the window. Near-0 = "
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"appeared once or twice across a long window. Undefined when "
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"corpus_span_days = 0; emit null/omit for single-day actors.",
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),
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"meta.first_seen_ts": _str(
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notes="ISO 8601 timestamp (with UTC offset, e.g. '2025-11-03T14:22:07+00:00') "
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"of the actor's earliest message in the corpus snapshot. Combined with "
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"last_seen_ts, this anchors corpus_span_days in absolute time so "
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"observations from different extractions can be compared temporally.",
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),
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"meta.last_seen_ts": _str(
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notes="ISO 8601 timestamp (with UTC offset, e.g. '2025-12-20T09:11:43+00:00') "
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"of the actor's latest message in the corpus snapshot. See first_seen_ts.",
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),
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"meta.fingerprint_confidence": _cat(
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"low", "medium", "high",
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notes="Qualitative reliability rating for this actor's full fingerprint. "
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"An attribution engine should weight all other observations from this "
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"actor proportionally to this value before compositing. Derivation is "
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"EXTRACTOR-DEFINED — the registry specifies the semantic contract, not "
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"the formula. Extractors must declare their heuristic in the source "
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"label (e.g. '#confidence-v1'). Typical inputs: total_messages, "
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"corpus_span_days, active_days, and any domain-specific thresholds "
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"the extractor authors have calibrated.",
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),
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# ── stylometric.* (motor analog — 13) ─────────────────────────────────
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"stylometric.punctuation_style": _hash(notes="canonical punctuation-pattern fingerprint"),
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"stylometric.capitalization_habit": _cat(
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"lowercase", "proper", "random_caps", "mixed_i",
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@@ -200,8 +261,23 @@ PRIMITIVE_REGISTRY: dict[str, ValueTypeSpec] = {
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"computation, performed once per extraction. Source label declares the "
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"top-K size and corpus tag (e.g. `#tfidf-top50`).",
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),
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"stylometric.pos_ngram_signature": _hash(
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notes="64-bit simhash over a POS n-gram (default bigram) frequency vector "
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"from the author's text corpus. Captures syntactic skeleton independent "
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"of vocabulary — an author can change every word they use and still "
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"retain the same POS-bigram fingerprint. ORTHOGONAL to character_ngram "
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"and function_word distributions: those capture surface form, this "
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"captures grammatical structure. Example signal: consistent ADJ-NOUN vs "
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"NOUN-ADJ ordering in Spanish, habitual ADV-VERB pre-position. "
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"TAGGER-DEPENDENT: source label MUST declare the tagger, language model, "
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"and n value (e.g. `#spacy-es_core_news_sm-bi` for spaCy Spanish "
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"small model, bigrams). Calibration note: chat-domain text is noisy — "
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"abbreviations, misspellings, and code-switching cause tagger errors "
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"that introduce fingerprint noise. Engines should weight low until "
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"calibrated against labelled chat corpora.",
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),
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# ── lexical.* (cognitive analog — 8) ──────────────────────────────────
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# ── lexical.* (cognitive analog — 11) ─────────────────────────────────
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"lexical.vocabulary_richness": _num(
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min_val=0.0, max_val=1.0,
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notes="Moving-Average Type-Token Ratio (MATTR) over a sliding window "
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@@ -242,6 +318,52 @@ PRIMITIVE_REGISTRY: dict[str, ValueTypeSpec] = {
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"market contexts where hierarchical and peer relationships are expressed "
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"through register choice.",
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),
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"lexical.dialect_region": _str(
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notes="Dominant regional variety of the actor's matrix language, expressed as "
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"a BCP-47 language-region tag (e.g. `es-CL`, `es-AR`, `es-MX`, `es-ES`, "
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"`en-US`). Detected from lexical marker density against per-region "
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"vocabulary tables; detection method and marker set version declared in "
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"source label (e.g. `#dialect-markers-v1`). Emit the literal string "
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"`unknown` when the extractor falls below its confidence threshold — do "
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"not omit the observation, so downstream engines can distinguish "
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"'undetected' from 'not extracted'. Language-agnostic in concept; the "
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"marker vocabulary is language-specific. COMPLEMENTARY to "
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"lexical.code_switching_matrix_language, which captures the dominant "
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"language via switching analysis rather than direct regional-marker lookup.",
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),
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"lexical.evaluative_morphology_density": _num(
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min_val=0.0, max_val=1.0,
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notes="Rate of evaluative morpheme tokens / total tokens. Evaluative morphology "
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"encompasses suffixes that add expressive/emotional loading to a stem: "
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"diminutives (`-ito`/`-ita`/`-cito`/`-cita` — affection, minimization, "
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"softening), augmentatives (`-ón`/`-ona`/`-ote`/`-ota` — intensification), "
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"pejoratives (`-ejo`/`-eja`/`-ucho`/`-ucha` — contempt), and intensives "
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"(`-azo`/`-aza` — force or admiration by context). Heavy diminutive use "
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"is characteristic of Mexican and Central American Spanish; River Plate "
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"speakers use them significantly less. The density is stable per-author "
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"and hard to consciously suppress — it is baked into language acquisition. "
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"Language-agnostic in concept; detection (suffix rules or morphological "
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"analyser) is language-specific. Source label declares the morpheme set "
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"and tool version (e.g. `#eval-morph-es-v1`).",
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),
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"lexical.optional_grammar_signature": _hash(
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notes="64-bit simhash over a vector of the author's preference probabilities "
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"at optional-grammar choice points — positions where the language offers "
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"multiple grammatically correct options and individual authors make stable "
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"idiosyncratic choices. For Spanish: compound past vs simple past ratio "
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"(`he comido` vs `comí` — Spain strongly prefers compound for recent "
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"actions; Latin America almost universally uses simple past, making this "
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"a high-reliability Spain/LatAm discriminator), subjunctive usage rate "
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"(avoidance correlates with informal register or non-native acquisition), "
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"leísmo/laísmo/loísmo clitic patterns (`le vi` vs `lo vi` for masculine "
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"accusative — leísmo is characteristic of Castilian Spain), and relative "
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"pronoun choice (`que` vs `el cual/la cual` — register marker). Each "
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"choice point is a scalar [0,1] probability; the simhash is computed over "
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"the concatenated vector. EXTRACTOR-DEFINED: choice-point set declared in "
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"source label (e.g. `#optgrammar-es-v1`). Requires sufficient corpus "
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"volume for stable probability estimates — thin corpora produce noisy "
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"hashes; engines should gate on meta.fingerprint_confidence before use.",
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),
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# ── temporal_evolution.* (lifecycle / change-over-time — 1) ───────────
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"temporal_evolution.lifecycle_phase": _cat(
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "behave-text"
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version = "0.1.1"
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version = "0.1.3"
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description = "BEHAVE-TEXT — text/messaging-domain behavioral observation registry, layered on behave-core"
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readme = "README.md"
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requires-python = ">=3.11"
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@@ -1,9 +1,9 @@
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# SPDX-License-Identifier: GPL-3.0-or-later
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"""Registry coverage tests for BEHAVE-TEXT.
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Asserts that every primitive listed in scratchpad.md's tables has exactly one
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Asserts that every primitive listed in README.md's tables has exactly one
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entry in PRIMITIVE_REGISTRY. Drift-detector — failing this test means
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scratchpad.md and the registry have diverged.
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README.md and the registry have diverged.
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"""
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from __future__ import annotations
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@@ -13,9 +13,18 @@ from pathlib import Path
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from behave_text.spec import PRIMITIVE_REGISTRY, ValueKind
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# Primitive paths expected by scratchpad.md (hand-extracted; v0).
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# Primitive paths expected by README.md (hand-extracted; v0).
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EXPECTED_PRIMITIVES = {
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# stylometric.* (motor analog — 8)
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# meta.* (corpus-snapshot footprint — 8)
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"meta.total_messages",
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"meta.corpus_span_days",
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"meta.msg_per_day",
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"meta.active_days",
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"meta.activity_density",
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"meta.first_seen_ts",
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"meta.last_seen_ts",
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"meta.fingerprint_confidence",
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# stylometric.* (motor analog — 13)
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"stylometric.punctuation_style",
|
||||
"stylometric.capitalization_habit",
|
||||
"stylometric.emoji_usage",
|
||||
@@ -28,7 +37,8 @@ EXPECTED_PRIMITIVES = {
|
||||
"stylometric.function_word_distribution_top200",
|
||||
"stylometric.character_ngram_simhash",
|
||||
"stylometric.distinctive_vocabulary_signature",
|
||||
# lexical.* (cognitive analog — 8)
|
||||
"stylometric.pos_ngram_signature",
|
||||
# lexical.* (cognitive analog — 11)
|
||||
"lexical.vocabulary_richness",
|
||||
"lexical.slang_density",
|
||||
"lexical.code_switching_rate",
|
||||
@@ -37,6 +47,9 @@ EXPECTED_PRIMITIVES = {
|
||||
"lexical.sentence_complexity_class",
|
||||
"lexical.question_formation_style",
|
||||
"lexical.imperative_style",
|
||||
"lexical.dialect_region",
|
||||
"lexical.evaluative_morphology_density",
|
||||
"lexical.optional_grammar_signature",
|
||||
# temporal_evolution.* (lifecycle/change-over-time — 1, added v0.2)
|
||||
"temporal_evolution.lifecycle_phase",
|
||||
# network.* (governance/role-shape — 2, added v0.3)
|
||||
|
||||
Reference in New Issue
Block a user