README.md

---
language:
  - sr
license: cc-by-nc-sa-4.0
pretty_name: "Serbian Morphological Segmentation Knowledge Base"
tags:
  - morphology
  - nlp
  - serbian
  - segmentation
  - linguistics
  - slavic
task_categories:
  - token-classification
  - text-generation
size_categories:
  - 10B<n<100B
---

# 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](https://huggingface.co/datasets/procesaur/kisobran) | MaCoCu, PDRS, SrpKorNews, CC100, CLASSLA, HPLT, mC4, OSCAR, srWaC |
| [procesaur/Vikipedija](https://huggingface.co/datasets/procesaur/Vikipedija) | Serbian Wikipedia |
| [procesaur/znanje](https://huggingface.co/datasets/procesaur/znanje) | enauka_sr, nardus_sr (scientific papers and theses) |
| [oscar-corpus/oscar](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/Nikola-92/sr-morpho-vocab) | Optimized 10K Serbian morpheme vocabulary for LLM injection |
| [`Nikola-92/Qwen2.5-7B-serbian-morpho`](https://huggingface.co/Nikola-92/Qwen2.5-7B-serbian-morpho) | Qwen 2.5 7B with the 10K vocabulary injected |

## Usage

```python
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)](https://creativecommons.org/licenses/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.