Khmer Segmenter

A deterministic, dictionary-based Khmer word segmenter for NLP preprocessing, search, formal documents, and embedded applications. It combines Khmer Unicode normalization, frequency-weighted Viterbi decoding, linguistic rules, and unknown-word recovery without a runtime machine-learning model.

Documentation · Data preparation · C port · Rust port · Live demo

[!IMPORTANT] The package includes an attributed Khmer dictionary and derived runtime data for noncommercial use. Project code is MIT licensed; the bundled linguistic data has separate terms in DATA_LICENSE.md.

[!WARNING] Hyphenation is experimental. Its dictionary and rules are still being refined, and many words do not yet receive correct internal break positions. Do not rely on hyphenation output for production typography without review.

Features

• Deterministic segmentation for the same code and local data
• Khmer Unicode normalization
• Frequency-weighted dictionary decisions
• Layered curated and supplemental segmentation lexicons
• RAC-curated spelling correction and autocomplete vocabulary
• Word spelling checks through Python and the CLI
• Whole-span typo diagnostics with Khmer-aware ranked suggestions
• Unknown-span preservation
• Typed token metadata with offsets and lexical POS candidates
• Experimental Khmer hyphenation
• Python API and khmer-segment CLI
• Shared KDIC/KHYP formats for C and Rust applications
• Rust/WASM segmentation and experimental spelling APIs for browser applications

This is a lexical segmenter, not a semantic parser or contextual POS tagger. pos_candidates are possibilities found in optional local lexical data.

Install for development

Python 3.10 or newer is required.

git clone https://github.com/Sovichea/khmer_segmenter.git
cd khmer_segmenter
python -m venv .venv

Activate the environment and install the src-layout package:

# Linux/macOS
source .venv/bin/activate
python -m pip install -e .

# Windows PowerShell
.venv\Scripts\Activate.ps1
python -m pip install -e .

After its first release, the distribution will install with:

pip install khmer-viterbi-segmenter

The import package remains khmer_segmenter. The bundled runtime data works immediately; no separate download is required.

Dictionary source and optional replacement

The original dictionary is published by Seanghay Hay (seanghay) on Hugging Face and was extracted from the Khmer Dictionary 2022 of the National Council of Khmer Language, Royal Academy of Cambodia:

https://huggingface.co/datasets/seanghay/khmer-dictionary-44k

The dataset may be redistributed for noncommercial use with attribution. The bundled normalized lexicons, RAC-only frequencies, lexical POS candidates, and experimental hyphenation pairs retain that credit and restriction. See the linguistic data notice.

For an exact model rebuild, download the structured RAC CSV directly from the original publisher:

mkdir -p dataset
curl -L \
  "https://huggingface.co/datasets/seanghay/khmer-dictionary-44k/resolve/525c0171894465cba920a9181387a032c11610d3/RAC-Khmer-Dict-2022.csv?download=true" \
  -o dataset/RAC-Khmer-Dict-2022.csv

Windows PowerShell:

New-Item -ItemType Directory -Force dataset | Out-Null
Invoke-WebRequest `
  -Uri "https://huggingface.co/datasets/seanghay/khmer-dictionary-44k/resolve/525c0171894465cba920a9181387a032c11610d3/RAC-Khmer-Dict-2022.csv?download=true" `
  -OutFile "dataset/RAC-Khmer-Dict-2022.csv"

Rebuild the authoritative RAC runtime artifacts deterministically:

python scripts/rebuild_rac_model.py \
  --rac-csv dataset/RAC-Khmer-Dict-2022.csv \
  --output-dir build/rac

khmer-segment data prepare --rac-tsv PATH remains available for simple custom 0.1-style dictionary overrides; it does not reproduce the strict RAC model.

The installed layered model additionally contains conservative supplemental segmentation chunks. Supplemental entries can preserve names, newer vocabulary, and known typo spans as single tokens, but they never become valid spellings or autocomplete candidates. Curated words keep their normal costs; supplemental words receive a cost penalty. Recreate that layer from a reviewed legacy list:

python scripts/prepare_supplemental_lexicon.py path/to/legacy_words.txt \
  --audit build/supplemental_audit.tsv
python scripts/build_dictionary_kdict.py

The audit records every curated match, retained chunk, and rejected fragment.

python scripts/validate_findings.py \
  --rac-csv dataset/RAC-Khmer-Dict-2022.csv

The Python resolver checks these locations in order:

1. data_dir= or CLI --data-dir
2. KHMER_SEGMENTER_DATA_DIR
3. The user data directory for the operating system
4. The data bundled with the installed package
5. khmer_segmenter/dictionary_data/ in a development checkout

Check the resolved files:

khmer-segment data status
khmer-segment data sources
khmer-segment data prepare --rac-tsv dataset/rac_dictionary_2022_pairs.tsv

See Prepare Dictionaries for Python, C, and Rust for frequency generation and KDIC/KHYP compilation.

Python API

from khmer_segmenter import KhmerSegmenter

segmenter = KhmerSegmenter()

tokens = segmenter.segment("ខ្ញុំស្រឡាញ់ប្រទេសកម្ពុជា")
print(tokens)

To use a replacement dictionary, set KHMER_SEGMENTER_DATA_DIR or pass data_dir= explicitly:

segmenter = KhmerSegmenter(data_dir="/path/to/replacement-data")

Typed analysis results include normalized offsets and optional lexical data:

for token in segmenter.analyze("ខ្ញុំសរសេរឯកសារ"):
    print(token.text, token.start, token.end, token.known)
    print(token.frequency, token.pos_candidates)
    print(token.spelling_valid)

Check whole words independently of segmentation:

segmenter.is_spelling_valid("នីមួយៗ")
segmenter.check_spelling(["នីមួយៗ", "ពាក្យមិនស្គាល់"])

Detect probable typos in continuous text:

diagnostics = segmenter.detect_typos("សម្បត្ត")

for diagnostic in diagnostics:
    print(diagnostic.text, diagnostic.start, diagnostic.end)
    for suggestion in diagnostic.suggestions:
        print(suggestion.text, suggestion.edit_cost, suggestion.edits)

For an explicit editor lookup, treat the complete input as one word rather than relying on its initial segmentation:

suggestions = segmenter.suggest_spelling("សសេរ")
print(suggestions[0].text)  # សរសេរ

This reports the whole input span សម្បត្ត, suggests សម្បត្តិ, and records an insertion of ិ at offset 7. Diagnostics are separate from segmentation tokens, so typo recovery does not silently alter segment() output. Offsets refer to normalized text by default; use normalize=False when the caller has already normalized the input.

Typo detection searches only near invalid Khmer tokens and uses weighted edits: dependent vowels and signs cost less than consonant substitutions. Results are probable corrections, not automatic replacements. Proper names, dialectal forms, and historical spellings still require application-level review.

Use a named spellcheck profile for application integration:

from khmer_segmenter import SpellcheckProfile

# Live editor underlines: strict confidence filtering and low latency.
diagnostics = segmenter.check_text(text, profile=SpellcheckProfile.TYPING)

# Explicit "Check document": broader OOV correction search.
diagnostics = segmenter.check_text(text, profile="document")

typing is the production default. document allows a wider edit distance but still avoids scanning every valid dictionary fragment. high-recall examines valid fragments and is intentionally experimental because it can produce many false positives. The old include_valid_fragments option remains as a low-level compatibility override.

Reviewed exact typo pairs live in dictionary_data/khmer_typo_corrections.tsv. Only approved rows affect spellcheck; proposed additions remain pending until reviewed. Run python scripts/sync_typo_corrections.py after editing the canonical Python copy so Rust and WASM consume the same list.

The legacy dictionary result remains available as segment_with_metadata(text).

Experimental hyphenation uses the bundled pairs by default. Many words are not yet separated correctly, so applications should treat its output as a suggestion and review it before display or publication:

from khmer_segmenter import KhmerHyphenator

hyphenator = KhmerHyphenator.from_data_dir()
result = hyphenator.hyphenate(
    "សហប្រតិបត្តិការ",
    segmenter=segmenter,
    separator="-",  # use "\u200b" for invisible break opportunities
)

CLI

Segment positional text, a file, or standard input:

khmer-segment segment "ខ្ញុំស្រឡាញ់ប្រទេសកម្ពុជា"
khmer-segment segment --input input.txt --output segmented.txt
cat input.txt | khmer-segment segment

Machine-readable output:

khmer-segment segment "ខ្ញុំសរសេរឯកសារ" --format json
khmer-segment analyze "ខ្ញុំសរសេរឯកសារ" --format json
khmer-segment spellcheck "នីមួយៗ ពាក្យមិនស្គាល់"
khmer-segment diagnose "សម្បត្ត" --format json
khmer-segment diagnose --profile document --input manuscript.txt --format json
khmer-segment diagnose "រស់ជាតិ" --profile high-recall --format json

analyze reports lexical candidates; it does not claim contextual POS tagging.

Experimental hyphenation and benchmarking:

khmer-segment hyphenate "សហប្រតិបត្តិការ" --visible-hyphen
khmer-segment benchmark --input dataset/my_corpus.txt --limit 1000

The hyphenate command is not production-ready: many words may contain incorrect or missing break positions.

Use a non-default local data directory with the global option before the subcommand:

khmer-segment --data-dir /path/to/local/data segment "អត្ថបទខ្មែរ"

Build the Python distribution

python -m pip install --upgrade build twine
python -m build
python -m twine check dist/*

The wheel contains code plus the attributed runtime data and its reproducibility manifest. Tests audit the archive to reject corpora, backups, provenance payloads, and unapproved linguistic artifacts.

Documentation

• Documentation index
• Data sources, attribution, and provenance
• Dictionary and embedded-data preparation
• Development workflows
• Evaluation
• Migration from 0.1.1
• Benchmarks
• Algorithm and porting reference

Data policy

The four runtime files listed in DATA_LICENSE.md are redistributed with attribution for noncommercial use. Source downloads, evaluation corpora, backups, provenance payloads, intermediate tables, and native build artifacts remain ignored and local.

Removing files from the current Git tree does not remove copies from old Git history. See the data policy before publishing or rewriting repository history.

License and acknowledgements

Project code is licensed under the MIT License. Bundled linguistic data is subject to the separate attribution and noncommercial notice.

Original data authors, authorities, corpus creators, and annotators are listed in Data Sources, Attribution, and Provenance.