| --- |
| 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. |
|
|
