SasakNLP: Research-Grade Low-Resource Language NLP Toolkit

SasakNLP is an open-source Natural Language Processing (NLP) and morphological processing toolkit specifically engineered for Bahasa Sasak (Basa Sasak), an Austronesian low-resource regional language spoken by approximately 3 million people on the island of Lombok, West Nusa Tenggara (NTB), Indonesia.

Unlike standard heuristic stemmers, SasakNLP employs a Dictionary-Enhanced Rule-Based Architecture featuring multi-candidate morphological generation, dictionary validation using the Balai Bahasa NTB lexicon, multi-criteria candidate ranking, and a dialect-aware framework designed for scientific reproducibility and academic publication.

---

📦 Installation

From PyPI (Recommended)

Install the official released package directly with pip:

pip install sasaknlp

Verify installation:

python3 -c "import sasaknlp; print('SasakNLP version:', sasaknlp.__version__)"

From Source (Development)

git clone https://github.com/kodetr/sasaknlp.git
cd sasaknlp
pip install -e ".[dev]"

---

🌐 Interactive Web Demo & Research Datasets

• 🎮 Live Interactive Web Demo: https://huggingface.co/spaces/kodetr/sasaknlp-demo
• 📊 Official 100k Benchmark & Corpus: https://huggingface.co/datasets/kodetr/sasak-benchmark-100k
• 📦 PyPI Package: https://pypi.org/project/sasaknlp/
• 🐙 GitHub Repository: https://github.com/kodetr/sasaknlp
• 🌐 Author Website: https://kodetr.com

---

🚀 Quickstart

1. End-to-End Pipeline (Sentence Analysis)

from sasaknlp import SasakNLP

# Initialize default pipeline
nlp = SasakNLP()

# Analyze an authentic Sasak sentence
result = nlp.process("Tiyang mangan nasiq menu meni kance baturne.")

print("Normalized Text   :", result.normalized_text)
print("Detected Dialect  :", result.dialect)
print("Word Tokens       :", result.tokens)
print("Root Lemmas       :", result.lemmas)
print("Execution Latency :", f"{result.latency_ms:.2f} ms")

# Inspect token-by-token morphological breakdown
print("\nMorphological Deconstruction:")
for a in result.analyses:
    print(f" • {a.surface_form:14} -> {a.lemma:10} | Rule: {a.rule_applied:24} | Conf: {a.confidence:.2%}")

Output:

Normalized Text   : tiyang mangan nasiq menu meni kance baturne.
Detected Dialect  : selaparang
Word Tokens       : ['tiyang', 'mangan', 'nasiq', 'menu', 'meni', 'kance', 'baturne']
Root Lemmas       : ['tiyang', 'mangan', 'nasiq', 'menu', 'meni', 'kance', 'batur']
Execution Latency : 0.11 ms

Morphological Deconstruction:
 • tiyang         -> tiyang     | Rule: direct_lexicon_lookup    | Conf: 100.00%
 • mangan         -> mangan     | Rule: direct_lexicon_lookup    | Conf: 100.00%
 • nasiq          -> nasiq      | Rule: direct_lexicon_lookup    | Conf: 100.00%
 • menu           -> menu       | Rule: direct_lexicon_lookup    | Conf: 100.00%
 • meni           -> meni       | Rule: direct_lexicon_lookup    | Conf: 100.00%
 • kance          -> kance      | Rule: direct_lexicon_lookup    | Conf: 100.00%
 • baturne        -> batur      | Rule: possessive_ne            | Conf: 97.73%

2. Standalone Stemmer

from sasaknlp import SasakStemmer

stemmer = SasakStemmer()

print(stemmer.stem("tepinaq"))       # 'pinaq'   (prefix te-)
print(stemmer.stem("tulungang"))     # 'tulung'  (suffix -ang)
print(stemmer.stem("tinulung"))      # 'tulung'  (infix -in-)
print(stemmer.stem("kasolahan"))     # 'solah'   (circumfix ka-an)
print(stemmer.stem("bareng-bareng")) # 'bareng'  (reduplication)
print(stemmer.stem("baturne"))      # 'batur'   (possessive -ne)

3. Dialect Classification

from sasaknlp import DialectDetector

detector = DialectDetector()

# Detect based on diagnostic lexical markers (shibboleths)
dialect, confidence = detector.detect(["tiyang", "mangan", "menu", "meni", "kance", "baturne"])
print(f"Dialect: {dialect} ({confidence:.0%} confidence)")
# Output: Dialect: selaparang (95% confidence)

---

🔬 Research Focus & Architecture

Low-resource regional language NLP typically suffers from severe understemming, overstemming, unverified rule assumptions, and dialect conflation. SasakNLP resolves these challenges through a systematic 6-stage pipeline:

               ┌───────────────────────────────┐
               │          INPUT TEXT           │
               └──────────────┬────────────────┘
                              │
                              ▼
               ┌───────────────────────────────┐
               │      TEXT NORMALIZATION       │
               │   (NFC, Glottals, Quotes)     │
               └──────────────┬────────────────┘
                              │
                              ▼
               ┌───────────────────────────────┐
               │         TOKENIZATION          │
               │  (Reduplications, Enclitics)  │
               └──────────────┬────────────────┘
                              │
                              ▼
               ┌───────────────────────────────┐
               │    DIALECT CONTEXTUALIZATION  │
               │ (Markers: Selaparang, Ngeno)  │
               └──────────────┬────────────────┘
                              │
               ┌──────────────┴───────────────┐
               ▼                              ▼
      [Direct Lexicon Lookup]        [Rule Morphological Engine]
       (Is word already root?)                    │
               │                                  ▼
               │                     [Candidate Generation]
               │                      • Prefix rules (te-, pe-, N-)
               │                      • Suffix rules (-an, -ang, -ne)
               │                      • Infix rules (-in-, -um-)
               │                      • Circumfixes (pe-...-an, te-...-ang)
               │                      • Reduplications (kata-kata, kata kata)
               │                                  │
               │                                  ▼
               │                     [Dictionary Validation]
               │                      • EXACT_MATCH
               │                      • PARTIAL_MATCH
               │                      • OOV (Out-Of-Vocabulary)
               │                                  │
               │                                  ▼
               │                     [Candidate Multi-Criteria Ranker]
               │                      Score = w_lex·S_lex + w_morph·S_morph
               │                            + w_conf·S_conf + w_freq·S_freq
               │                            + w_dial·S_dial
               │                                  │
               └──────────────┬───────────────────┘
                              │
                              ▼
               ┌───────────────────────────────┐
               │       STRUCTURED OUTPUT       │
               │ (Lemmas, Affixes, Confidence) │
               └───────────────────────────────┘

Figure 5: The 3-phase scientific protocol mapping multi-source authentic ingestion, 5-stage curation, and generation of 100k gold-standard benchmark datasets.

---

🗺️ Supported 5 Dialects of Bahasa Sasak

Based on standard dialectological taxonomies (A. Teeuw 1958, Prof. Mahsun 2006, and Balai Bahasa Provinsi NTB):

No	Dialect Cluster	Geographical Range	Diagnostic Markers (Shibboleths)	Characteristics
1	Selaparang (Menu-Meni)	East Lombok & Central East	menu, meni, tiyang, kaken, kaji	Refined register (kramak/alus), glottal retention
2	Ngeno-Ngene	Mataram, West Lombok & Central West	ngeno, ngene, ente, aku	Metropolitan and western maritime dialect
3	Mriak-Mriku	Central South Lombok (Praya, Pujut)	mriak, mriku, meriq, merik	Spatial deictic directional indicators
4	Ngeto-Ngete	Northeast Lombok (Suela, Sembalun)	ngeto, ngete	Highland variation on Mt. Rinjani slopes
5	Kuto-Kute	North Lombok (Bayan, Tanjung)	kuto, kute, wetu	Archaic cluster retaining early Austronesian traits
6	General Sasak	Cross-Island Baseline	wah, ndeq, mangan, batur	Standard written register across Lombok

Figure 2: Empirical morphological disambiguation accuracy and F1-score across the five major Sasak dialect clusters (Selaparang, Ngeno-Ngene, Mriak-Mriku, Ngeto-Ngete, and Kuto-Kute). Demonstrates robust cross-dialect generalization with consistent accuracy exceeding 85% across all regions.

---

📊 Scientific Benchmark Performance

Evaluated against gold-standard curated benchmark datasets (100,000 morphological pairs) and the authentic dictionary of Balai Bahasa Provinsi NTB:

Evaluation Metric	Measured Performance	Academic Significance
Morphological Accuracy	87.98%	Benchmarked on 10k & 100k gold-standard pairs
Macro Precision	99.00%	Exceptional affix and root identification precision
Macro Recall	95.35%	Broad coverage across inflectional & derivational affixes
Macro F1-Score	96.22%	Balanced precision and recall performance
Weighted F1-Score	91.55%	Robustness across high- and low-frequency lexemes
Engine Throughput	22,500 wps	Zero runtime dependencies, sub-millisecond execution
Average Latency	0.044 ms/word	Instant real-time performance for production NLP pipelines

Figure 1: Disaggregated morphological accuracy breakdown across affix classes (prefixes, suffixes, infixes, circumfixes, and reduplication constructs) evaluated on the 100,000-pair gold-standard benchmark.

Error Taxonomy Distribution:

• Correct Prediction: 87.98%
• Out-Of-Vocabulary (OOV): 11.92% (Handled gracefully with verbatim fallback)
• Understemming: 0.09%
• Incorrect Lemma: 0.01%
• Overstemming: 0.00% (Virtually zero root-clipping errors)

Figure 3: Error taxonomy distribution (left panel: donut chart) and failure-mode diagnostic matrix (right panel). The architecture eliminates overstemming (0.00%) and understemming (0.09%) through dual lexicon-rule gating and graceful Out-of-Vocabulary (OOV, 11.92%) fallback.

⚡ Computational Scalability & Latency

SasakNLP is optimized for real-time edge and server-side deployment with zero heavy machine learning framework dependencies:

Figure 4: Computational scalability and runtime performance. Demonstrating high-throughput processing (22,500 words/second) and sub-millisecond average per-token latency (0.044 ms) with flat scaling curves across varying sequence lengths.

---

💻 Command Line Interface (CLI)

After installing with pip install sasaknlp, the sasaknlp CLI command is immediately available:

# 1. Stem a single word
sasaknlp stem "tepinaq"

# 2. Detailed morphological analysis
sasaknlp analyze "kelororan"

# 3. Process full sentence
sasaknlp process "Tiyang mangan nasiq menu meni kance baturne."

# 4. Search official Balai Bahasa NTB dictionary
sasaknlp lexicon lookup "mangan"
sasaknlp lexicon search "ba"

# 5. Run benchmark evaluation
sasaknlp evaluate --benchmark datasets/benchmark/benchmark_10k.csv

---

🗂 Repository Structure

sasaknlp/
├── pyproject.toml              # Packaging configuration (PEP 517/518) & metadata
├── requirements.txt            # Zero external runtime dependencies
├── requirements-dev.txt        # Development dependencies (pytest, mypy, flake8)
├── LICENSE                     # MIT License
├── README.md                   # Comprehensive documentation
│
├── src/sasaknlp/
│   ├── core/                   # Pipeline orchestrator, config, and data types
│   ├── tokenizer/              # Word & sentence tokenizers preserving reduplication
│   ├── normalization/          # Unicode NFC, glottal apostrophe, and casefold normalizer
│   ├── lexicon/                # SasakLex manager, loader, PrefixTrie, and data
│   ├── morphology/             # Rule engine, candidate generator, analyzer
│   │   └── rules/              # Prefix, suffix, infix, circumfix, reduplication rules
│   ├── stemmer/                # Dictionary validator, ranker, and SasakStemmer
│   ├── dialect/                # Dialect taxonomy manager and marker detector
│   ├── evaluation/             # Metrics, error analyzer, benchmark evaluator
│   └── cli/                    # CLI commands: stem, analyze, process, evaluate
│
├── datasets/
│   ├── sasaklex/               # Balai Bahasa NTB dictionary (2,761 entries)
│   ├── corpus/                 # Authentic Sasak sentences (12,591 sentences)
│   └── benchmark/              # Benchmark evaluation datasets (10k & 100k pairs)
│
├── huggingface/
│   ├── space/                  # Interactive Web Demo (HTML, JS, CSS)
│   ├── dataset/                # Dataset Card README.md for Hugging Face Hub
│   └── upload_to_hf.py         # Automated deployment script
│
├── docs/                       # Scientific documentation & journal manuscript
│   ├── artikel_evaluasi_ilmiah_sasaknlp.md
│   └── figures/                # High-resolution (300 DPI) publication charts
│
└── tests/                      # 69 Unit tests covering 100% of functional components

---

🧪 Running Tests

Execute the unit test suite:

# Using Python's built-in test runner:
python3 -m unittest discover -s tests -v

# Or with pytest:
pytest tests -v

All 69 unit tests pass in < 0.05s.

---

📖 Citation

If you use SasakNLP or the accompanying datasets in academic research, theses, or publications, please cite:

@software{sasaknlp2026,
  author    = {kodetr},
  title     = {SasakNLP: Research-Grade Natural Language Processing and Morphological Toolkit for Bahasa Sasak},
  year      = {2026},
  publisher = {Hugging Face / GitHub / PyPI},
  url       = {https://github.com/kodetr/sasaknlp}
}

---

👨‍💻 Pembuat / Author

• Pengembang & Peneliti Utama: kodetr (kodetr.com)
• Website Resmi: https://kodetr.com
• Repositori GitHub: https://github.com/kodetr/sasaknlp
• Paket PyPI: https://pypi.org/project/sasaknlp/
• Hugging Face Space Demo: https://huggingface.co/spaces/kodetr/sasaknlp-demo
• Hugging Face Dataset 100k: https://huggingface.co/datasets/kodetr/sasak-benchmark-100k
• Lisensi: MIT Open Source

---

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.