Serbian Morphological Segmentation Knowledge Base (sr-morpho-base)

A comprehensive morphological knowledge base for the Serbian language, produced by a multi-stage NLP pipeline that mines productive morphemes from dictionary data and generalizes them across a large text corpus.

Dataset Description

This repository contains two classes of artifacts:

1. Morphological knowledge base — the complete output of the segmentation pipeline: segmented word forms, frequency files for all morpheme types, and the supporting knowledge structures used during segmentation.
2. Raw corpus — corpus.txt, a 60+ GB unified Serbian text corpus in both Latin and Cyrillic scripts, assembled from multiple open sources.

What is in this repository

File	Description
corpus.txt	Unified Serbian corpus (~60 GB, Latin + Cyrillic, one document per line)
knowledge_base_final.tsv	Main output: merged segmentations for all known and discovered words
all_forms_segmented.tsv	Dual-script, dual-root segmentation from dictionary only
unknowns_segmented.tsv	Segmentation results for corpus-discovered unknown words
lemma_profiles.json	Complete morphological profile for every dictionary lemma
root_map.json	Map from each root surface form to its associated lemmas
canonical_root_map.json	Data-driven mapping from non-canonical to canonical root variants
candidate_prefixes.txt / candidate_suffixes.txt	Discovered productive affixes
stable_lemmas.txt	All dictionary lemmas (used as phonological guard)
known_forms_frequencies.tsv	Corpus frequency counts for known word forms
known_roots_frequencies.tsv	Corpus frequency counts for known roots (surface)
known_roots_abstract_frequencies.tsv	Corpus frequency counts for abstract (canonical) roots
known_prefixes_frequencies.tsv	Corpus frequency counts for known prefixes
known_suffixes_frequencies.tsv	Corpus frequency counts for known suffixes
unknowns_forms_frequencies.tsv	Same, for corpus-discovered unknown words
unknowns_roots_frequencies.tsv
unknowns_roots_abstract_frequencies.tsv
unknowns_prefixes_frequencies.tsv
unknowns_suffixes_frequencies.tsv

Corpus Sources

corpus.txt was assembled from the following open-source Serbian datasets:

Source	Description
procesaur/kisobran	MaCoCu, PDRS, SrpKorNews, CC100, CLASSLA, HPLT, mC4, OSCAR, srWaC
procesaur/Vikipedija	Serbian Wikipedia
procesaur/znanje	enauka_sr, nardus_sr (scientific papers and theses)
oscar-corpus/oscar	OSCAR unshuffled deduplicated Serbian

All sources were sentence-tag-stripped, NFC-normalized, and deduplicated before merging.

Pipeline Architecture

The segmentation pipeline operates in 9 stages:

1. Dictionary mining (updated_srpmd_pipeline.py) — parses DELA-style Serbian dictionaries, mines phonological alternations, discovers abstract roots, and segments all known forms with dual-script support.
2. Affix discovery (discover_affixes_from_known.py) — identifies productive prefixes and suffixes by measuring stem variety.
3. Corpus parsing (parser.py) — scans corpus.txt in parallel, counting known morpheme frequencies and collecting unknown words.
4. Frequency merging (merge.py) — consolidates frequency shards.
5. Unknown consolidation (consolidate_unknowns.py) + Canonical map (build_canonical_map.py).
6. Unknown segmentation (segment_unknowns_phonologically.py) — applies phonological rules using all knowledge bases.
7. Unknown frequency counting (count_unknown_frequencies.py).
8. Unknown frequency merging (merge_unknowns.py).
9. Knowledge base merge (merge_knowledge.py).

Phonological Engine

The pipeline implements a cascade of Serbian-specific phonological reversal rules:

• Consonant deletion (Gubljenje suglasnika)
• Fleeting vowel removal (Nepostojano A)
• L-vocalization (Prelazak L u O)
• Place assimilation (Jednačenje po mestu tvorbe)
• Voicing assimilation (Jednačenje po zvučnosti)
• Sibilarization and Palatalization
• Iotation (Jotovanje)
• Data-driven canonical root correction

Related Repositories

Repository	Description
Nikola-92/sr-morpho-vocab	Optimized 10K Serbian morpheme vocabulary for LLM injection
Nikola-92/Qwen2.5-7B-serbian-morpho	Qwen 2.5 7B with the 10K vocabulary injected

Usage

import pandas as pd

# Load the main knowledge base
kb = pd.read_csv("knowledge_base_final.tsv", sep="\t")
print(kb.head())

# Load root frequencies
roots = pd.read_csv("known_roots_frequencies.tsv", sep="\t",
                    header=None, names=["root", "frequency"])
print(roots.nlargest(20, "frequency"))

Citation

If you use this dataset in your research, please cite:

@dataset{jankovic2025srmorpho,
  author    = {Jankovic, Nikola},
  title     = {Serbian Morphological Segmentation Knowledge Base},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/Nikola-92/sr-morpho-base}
}

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

You are free to share and adapt this material for non-commercial purposes, provided you give appropriate credit and distribute your contributions under the same license.