---
license: cc-by-sa-4.0
language:
- tr
pretty_name: Turkish Text Normalization Resources
tags:
- text-normalization
- lexical-normalization
- turkish
- deasciification
size_categories:
- 100K<n<1M
viewer: false
---

# Turkish Text Normalization Resources

Part of a Turkish text preprocessing toolkit built for CMPE 561 (Natural Language Processing), Application Project 1, Boğaziçi University, 2026. All code is our own; no external NLP library is used in the systems. Status: progress report, 20 October 2026.

These are the resources used by our rule- and corpus-based normalizer, which converts noisy or non-standard Turkish
(missing Turkish characters, chat abbreviations, repeated letters, colloquial suffixes, attached clitics, wrong casing)
into standard Turkish orthography. Companion repositories:
[stopwords](https://huggingface.co/datasets/eminesener/stopwords),
[sentence_splitter](https://huggingface.co/datasets/eminesener/sentence_splitter).

## How the normalizer uses the data

1. **Text-level cleanup** (Unicode NFC, invisible characters, dotted i, quote/apostrophe variants, whitespace,
   repeated punctuation): fixed rules, no data.
2. **Sentence state:** is the sentence ASCII-typed, noisy (at least 10% of lowercase words unknown to `wiki_vocab.tsv`)
   or all caps?
3. **Word cascade**, first stage that applies wins; every proposal must be a word attested in Wikipedia:
   (1) `normalization.tsv` lexicon → (2) repeated letters → (3) diacritic restoration by Wikipedia frequency →
   (4) colloquial suffix rules → (5) proper noun + case suffix → (6) clitic splitting (mi/de/ki) →
   (7) character 6-gram language model for unknown ASCII words. Stages 4–7 run only on noisy text.
4. **Casing** from the most frequent Wikipedia spelling; **context step** for ambiguous pairs (su/şu) with
   `wiki_context.tsv`; question-particle vowel harmony.

See `figures/flow_overview.png` and `figures/flow_cascade.png` (labels in Turkish).

## Files

| File | Format | Contents | Used in |
|---|---|---|---|
| `lexicon/normalization.tsv` | `surface ⇥ normalized ⇥ source` | 550 entries: 492 word pairs derived from MultiLexNorm Turkish **train** + 58 hand-written | cascade stage 1 (`slm → selam`) |
| `lexicon/normalization_manual.tsv` | same | 58 hand-written entries (chat abbreviations, spoken forms, spelling variants) | merged into the lexicon; wins on conflict |
| `wikipedia/wiki_vocab.tsv` | `word ⇥ freq` | 654,804 case-sensitive word forms seen ≥ 3 times in 267,494 Turkish Wikipedia articles (59.6M tokens) | validity check, diacritic restoration, casing, rule validation, char-LM training |
| `wikipedia/wiki_context.tsv` | `word ⇥ L/R ⇥ neighbour ⇥ count` | left/right neighbour counts for 5,240 diacritic-ambiguous words (neighbours ASCII-folded) | context step (`su an → şu an`) |
| `results/` | JSON, Markdown | all dev/test scores, ablation, error categories | |
| `figures/` | PNG | test accuracy vs. leave-as-is baseline, ablation, social-media error categories, flow diagrams | |

Lines starting with `#` in the TSV files are provenance comments.

Examples from `wiki_vocab.tsv`: `çok 120822`, `cok 113`, `Ankara 28265`, `ANKARA 81`, `ankara 16`, `Ankara'da 3705`.
Example from `wiki_context.tsv`: `şu R an 2338` vs. `su R an 7`.

## Results (test, scored once)

| Test case | Accuracy | Leave-as-is baseline | ERR |
|---|---|---|---|
| BOUN test, clean (does it damage good text?) | 99.56% | 100% | – |
| BOUN test, diacritics removed (synthetic) | 98.27% | 62.0% | 0.954 |
| BOUN test, all caps (synthetic) | 95.65% | 18.6% | 0.947 |
| MultiLexNorm Turkish test (real tweets) | 80.48% | 64.0% | 0.458 |

Thresholds were tuned on BOUN dev and on MultiLexNorm train (5-fold cross-validation, lexicon rebuilt from the
training folds in every fold). No test sentence was used to build any resource.

## Sources

| Source | Licence | Use |
|---|---|---|
| Turkish Wikipedia, dump 20231101 ([wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia), `20231101.tr`, shard 1 of 2) | CC BY-SA 3.0 / GFDL | `wiki_vocab.tsv`, `wiki_context.tsv`, char LM |
| MultiLexNorm Turkish ([bitbucket.org/robvanderg/multilexnorm](https://bitbucket.org/robvanderg/multilexnorm), `data/tur`, commit 31695c6); Çolakoğlu, Sulubacak & Tantuğ 2019, ACL SRW, [doi:10.18653/v1/P19-2037](https://doi.org/10.18653/v1/P19-2037); van der Goot et al. 2021, W-NUT | not stated; raw files **not** redistributed here | train: lexicon pairs, tuning; test: evaluation only |
| UD Turkish-BOUN ([UniversalDependencies/UD_Turkish-BOUN](https://github.com/UniversalDependencies/UD_Turkish-BOUN)); Türk et al. 2022, LRE | CC BY-SA 4.0 | evaluation text only (dev: tuning, test: scored once) |

If you use the lexicon, please also cite the MultiLexNorm and Çolakoğlu et al. papers.

## Main literature

Torunoğlu & Eryiğit 2014 (cascaded normalization of Turkish social media, LASM); Eryiğit & Torunoğlu-Selamet 2017
(NLE); Adalı & Eryiğit 2014 (vowel and diacritic restoration, LASM); Arslan 2016 (deASCIIfication for IR, IP&M);
Çolakoğlu et al. 2019; van der Goot et al. 2021.