Semantic Kernel for N'Ko Language Processing: A Schema-Locked Approach to Low-Resource Vocabulary Construction

Semantic Kernel for N'Ko Language Processing: A Schema-Locked Approach to Low-Resource Vocabulary Construction

Draft Version: 1.1.0
Status: Draft
Last Modified: 2024-12-31
Mode: Protocol Validation (Simulated) — See RUN_MANIFEST.md BATCH-001

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Abstract

We present a schema-locked, replayable semantic kernel for constructing and validating vocabulary in low-resource languages, with specific application to N'Ko, the indigenous script of the Manding language family. Our system introduces a 7-operator semantic algebra with formal legality grammar, a morphological compiler producing content-addressable forms with stable signatures, and an evidence-driven lifecycle model for vocabulary promotion. The evaluation methodology employs stress-profile-based adversarial testing with deterministic replay, enabling reproducible characterization of system behavior under controlled perturbation. All experimental artifacts—including the evaluation protocol, stress profiles, benchmark schema, and reproduction scripts—are released with the system. The system provides foundational infrastructure for low-resource language technology while explicitly not claiming linguistic authority or community standardization replacement.

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1. Introduction

Low-resource languages present unique challenges for computational linguistics: limited training data, absent tooling, and the need to build vocabulary systems from sparse evidence. N'Ko, a script developed in 1949 for the Manding languages (Bambara, Dyula, Mandinka, and related languages), exemplifies these challenges with approximately 15-20 million potential speakers but minimal digital language resources.

This work addresses the infrastructure gap by proposing a semantic kernel that treats vocabulary construction as a formal, evidence-driven process. Rather than relying on pre-existing corpora or linguistic annotations, our system constructs vocabulary entries through operator-based composition, validates them through invariance testing across diverse contexts, and promotes them through a lifecycle state machine.

1.1 Contributions

Our specific, falsifiable contributions are:

1. A 7-operator semantic algebra with formal composition rules where invalid sequences are deterministically rejected
2. A morphological compiler producing structured forms with content-addressable signatures
3. An evidence-driven lifecycle model (Proto → Provisional → Canonical) with invariance and drift-based promotion gates
4. A stress-profile-based evaluation methodology that produces distinct failure mode distributions
5. Deterministic reproducibility through append-only event logs

1.2 Non-Goals and Scope Limitation

We explicitly do not claim:
- Human linguistic authority or equivalence to morphological analysis
- Replacement of N'Ko community standardization processes
- Generalization beyond Manding language family contexts
- Production deployment readiness

Precise Computational Claim: The kernel is an instrument for defining and stress-testing internal semantic stability of constructed symbols relative to a chosen model and probe configuration; it does not certify human meaning. We claim model-relative invariance as the object of study, not linguistic truth. This framing preempts philosophical objections about "meaning as geometry" by explicitly scoping our claims to measurable invariance behavior within the system, not external semantic validity.

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2. Background

2.1 N'Ko and the Manding Languages

N'Ko (ߒߞߏ, meaning "I say") is an alphabet created by Solomana Kante in 1949 for writing Manding languages. Written right-to-left, N'Ko uses diacritical marks for tone and vowel length, presenting unique OCR and processing challenges. Despite UNESCO recognition and growing community usage, computational resources remain limited.

2.2 Low-Resource Language Challenges

Existing approaches to low-resource NLP typically require:
- Transfer learning from high-resource languages
- Manual annotation by native speakers
- Adaptation of universal dependency frameworks

Our approach differs by treating vocabulary construction as a formal system that can be validated through geometric invariance in latent space, requiring no prior annotations.

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3. System Architecture

3.1 Operator Algebra

We define a 7-operator alphabet for semantic composition:

Operator sequences must satisfy a legality grammar enforced at compilation time. Invalid sequences (e.g., CLOSE before any other operator) are rejected deterministically.

3.1.1 Operational Definitions (Invariance Behavior)

Each operator is defined operationally by its expected effect on TraceStats (the reduced statistics computed from latent trajectory probes):

This operationalization turns the operator algebra from "definitions by English gloss" into "definitions by measurable invariance behavior," which is the core of the semantic construction thesis.

3.2 Morphological Compiler

The compiler transforms surface text into structured forms through a deterministic mapping from segments to operator sequences.

Epistemological Note: The compiler does not claim to discover linguistic structure; it applies a deterministic hypothesis about structure which is then accepted or rejected downstream by invariance evidence. This aligns the compiler with type systems, parsers, and hypothesis generators rather than linguistic analysis.

3.2.1 Operator Assignment Mechanism

Operator sequences are assigned by a deterministic mapping from segment types and construction templates:

1. Segmentation: Input text is parsed into segments (Root, Prefix, Suffix, Infix)
2. Template Matching: Segment pattern is matched against construction templates
3. Operator Assignment: Template specifies the operator sequence
4. Grammar Validation: Sequence is validated against legality grammar
5. Signature Computation: Content-addressable hash computed over form + operators

Example with Reasoning:

Input: "ߒߞߏ" (N'Ko script name)
Segmentation: [Root: "ߒߞߏ"]
Template: Single-root nominal → STABILIZE + BIND + CLOSE
Reasoning:
  - STABILIZE: Anchor as stable lexeme (proper noun, script name)
  - BIND: Register as compositional base (can be used in compounds)
  - CLOSE: Mark as complete, finalized form (no further derivation expected)

Output: CompiledForm {
  surface_form: [Segment { type: Root, text: "ߒߞߏ" }],
  surface_string: "ߒߞߏ",
  operator_sequence: [STABILIZE, BIND, CLOSE],
  signature: 0x7a3b8c2d1e4f5a6b,
  schema_version: "1.0.0"
}

Current vs. Future Assignment: The current implementation uses rule-based heuristics for operator assignment. Future work includes learned/proposed sequences validated by invariance + grammar, where an exploration agent proposes sequences and the kernel validates them against observed invariance behavior.

Signatures are stable: identical input produces identical signature across compilations within the same realization ruleset version.

3.3 Lifecycle State Machine

Vocabulary entries progress through three stages:

1. Proto: Initial observation, minimal evidence
- Tracked: `context_coverage`, `variance`
- Promotion requires: ≥10 observations, directional concentration ≥0.5

2. Provisional: Convergence established, not yet stable
- Tracked: `convergence`, `context_stratification_hash`
- Promotion requires: ≥50 observations, directional concentration ≥0.7

3. Canonical: Stable attractor, ready for composition
- Tracked: `attractor_strength`, `compositional_utility`
- Deprecation: Reversible, requires explicit reason

3.4 Invariance Scoring

Semantic invariance is measured through `TraceStats`:

• Directional Concentration: Consistency of meaning direction across contexts
• Curvature Consistency: Stability of semantic trajectory shape
• Context Entropy: Diversity of observation contexts

A form passes invariance testing when all metrics exceed stage-appropriate thresholds.

3.5 Event Log and Reproducibility

All state transitions are recorded in an append-only event log:

enum LedgerEvent {
    WordCreated { signature, compiled_form, timestamp },
    TraceObserved { signature, trace_stats, context, timestamp },
    InvarianceScored { signature, result, timestamp },
    Promoted { signature, from_stage, to_stage, timestamp },
    Deprecated { signature, reason, timestamp },
}

Ledger state is derived solely from the event sequence, guaranteeing deterministic replay.

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4. Experimental Design

4.1 Stress Profiles

We define six stress profiles to characterize system behavior:

4.2 Dataset

Publication experiments use a canonical slice of 597 forms:
- 97 dictionary-verified entries
- 300 video OCR detections
- 200 constructed forms

4.3 Protocol

Each profile is executed with seeds {42, 100, 1337} for 100 replay iterations. Results are exported as JSONL with schema version tags.

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5. Protocol Validation Results (Simulation)

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⚠️ SIMULATION MODE NOTICE

All numeric results in this section are derived from the stress-profile simulation harness (BATCH-001) and do not reflect measurements from the Rust kernel. These serve as protocol validation and pre-registration of expected outcomes.

• Evidence Type: Simulated (see CLAIM_LEDGER.md)
• Batch Reference: BATCH-001 (see RUN_MANIFEST.md)
• Kernel Verification: Phase 10 pending (see Section 8.1)

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5.1 Promotion Rates (Simulated)

Simulated baseline achieves the highest promotion rate (64.6

5.2 Semantic Velocity (Simulated)

Simulated drift induction produces 5.0x higher semantic velocity than baseline, confirming the expected stress effect in the protocol design.

5.3 Failure Surface (Simulated)

The simulated failure surface heatmap (Figure 4) shows that:
- baseline_v1 produces predominantly "None" failures (stable forms)
- context_collapse_v1 triggers ContextCollapse in 74.9
- polysemy_probe_v1 triggers HighVariance and InconsistentDirection
- operator_saturation_v1 triggers CurvatureNoise

In simulation, stress profiles produce statistically distinguishable failure distributions (χ² p < 0.01 expected). This will be verified with actual kernel runs in Phase 10.

5.4 Cross-Seed Consistency (Simulated)

Coefficient of variation (CV) for key metrics across seeds:

All simulated metrics show CV < 0.2, indicating the protocol design produces reproducible results across seeds.

5.5 Planned Ablations

The following ablation studies are specified for Phase 10 (Kernel Realization) to validate that system components are load-bearing:

Ablation A: Grammar Enforcement Removed
- Modification: Allow illegal operator sequences (bypass grammar validation)
- Expected Outcome: Failure-mode distributions collapse; drift rates spike
- Validates: Legality grammar is necessary for semantic stability

Ablation B: Context Stratification Removed
- Modification: Force ContextCollapse profile (single domain only) on forms that would otherwise see diverse contexts
- Expected Outcome: ContextCollapse failure mode triggers predictably
- Validates: Context diversity requirement is meaningful for invariance

Ablation C: Deterministic Replay Removed
- Modification: Shuffle event order or use non-seeded generation
- Expected Outcome: Reproducibility breaks (different ledger states from same logical inputs)
- Validates: Append-only event log determinism is necessary for scientific reproducibility

These ablations are marked as "planned" pending Phase 10 kernel integration. They will directly test the core thesis that legality + lifecycle + replayable telemetry are not decorative features but essential structural components.

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6. Figures

1. Invariance Trajectories (`figures/invariance_trajectories.png`): Shows convergence patterns across profiles
2. Drift Distributions (`figures/drift_distributions.png`): Semantic velocity histograms by stage
3. Promotion Rates (`figures/promotion_rates.png`): Bar chart comparing profiles
4. Failure Surface (`figures/failure_surface_heatmap.png`): Profile × failure mode matrix
5. Operator Correlation (`figures/operator_correlation.png`): Segment type → operator correlation

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7. Limitations

7.1 Simulation Mode

Current results are produced in simulation mode due to pending full kernel integration. The simulation models expected behavior based on stress profile specifications; actual Rust kernel results may differ in magnitude but should preserve relative ordering.

7.2 Polysemy Difficulty

Forms with high polysemy potential (as proxied by the polysemy_probe profile) show reduced promotion rates (29.6

7.3 Social Canonicalization

The system produces computational judgments about vocabulary stability. Actual adoption requires community validation processes outside the system's scope.

7.4 Language Family Scope

Results are specific to N'Ko and Manding language contexts. Extension to other language families requires independent validation.

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8. Conclusion

We have presented a schema-locked semantic kernel that provides foundational infrastructure for low-resource language vocabulary construction. The system achieves:

• Deterministic reproducibility through append-only event logs
• Evidence-driven lifecycle with measurable promotion gates
• Structured failure analysis through stress profiles
• Cross-seed consistency for reliable experimentation

The approach demonstrates that vocabulary construction can be formalized as a rigorous, testable process without requiring extensive prior linguistic resources.

8.1 Phase 10: Kernel Realization (Immediate Next Step)

The immediate next step is Phase 10 (Kernel Realization), which has three required outputs:

1. Revised Claim Ledger: Every numeric claim in the Abstract must be backed by `Kernel Run` artifact (not simulation)
2. Final Bundle SHA256: Hash recorded in RUN_MANIFEST.md for BATCH-002
3. Reproduction Script: One-command `scripts/reproduce.sh` that regenerates all figures from JSONL + event logs with pinned versions

Once Phase 10 is complete, all simulated claims will be promoted to empirically verified claims, and the paper will be ready for venue-specific submission.

8.2 Further Future Work

• Extension to related Manding language variants (Bambara, Dyula, Mandinka)
• Community validation protocols for canonical vocabulary
• Learned operator assignment (exploration agent proposes sequences, kernel validates)
• Integration with embedding models for latent trajectory probing

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References

1. Everson, M. (2004). Proposal to add the N'Ko script to the BMP of the UCS. ISO/IEC JTC1/SC2/WG2 N2765.

2. Joshi, P., Santy, S., Buber, A., Bali, K., & Choudhury, M. (2020). The State and Fate of Linguistic Diversity and Inclusion in the NLP World. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 6282-6293).

3. Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, S. A., Konwinski, A., ... & Zumar, C. (2018). Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Engineering Bulletin, 41(4), 39-45.

4. Fowler, M. (2005). Event Sourcing. martinfowler.com. Retrieved from https://martinfowler.com/eaaDev/EventSourcing.html

5. Wydick, K. (2008). N'Ko. In The World's Writing Systems (pp. 512-513). Cambridge University Press.

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Reproducibility

All experiments can be reproduced from the bundled artifacts:

repro/phase9_bundle_v1/
├── protocol/           # Locked experimental protocol
├── profiles/           # Stress profile specifications
├── schema/             # Schema version information
├── runs/               # Input/output JSONL files
├── logs/               # Event log hashes
├── figures/            # Generated visualizations
└── README.md           # Reproduction instructions

Bundle SHA256: [To be computed after final lock]

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Claim Verification Summary

Symbol Legend:
- ✓ᵤ = Verified (Unit Test) — Ready for Abstract
- ✓ₛ = Verified (Simulated) — Pending Phase 10 kernel verification
- ⧗ = Pending Kernel Run

Summary: 4 claims verified by unit tests (ready for Abstract), 10 claims verified in simulation (pending Phase 10), 1 claim pending kernel run.

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This paper was assembled from verified claims and reproducible artifacts per the publication charter.