| --- |
| license: mit |
| --- |
| |
| # Speliuk |
|
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| A more accurate spelling correction for the Ukrainian language. |
|
|
| ## Motivation |
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| When using a spell checker in systems that perform an automatic spelling correction without human verification, the following questions arise: |
| - How to avoid false correction, i.e. when a real word that is not present in a vocabulary is corrected? This is especially viable for fusional languages such as Ukrainian. |
| - How to find a single best correction for a misspelled word? Many spell checkers rely on the frequency of candidates and their edit distance discarding the surrounding context. |
|
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| To address these issues, we propose a system that is compatible with any spell checker but focuses on precision over recall.<br> |
| We improve the accuracy of a spell checker by using these complimentary models: |
| - [KenLM](https://github.com/kpu/kenlm). The model is used for fast perplexity calculation to find the best candidate for a misspelled word. |
| - Transfomer-based NER pipeline to detect misspelled words. |
| - [SymSpell](https://github.com/wolfgarbe/SymSpell). As of now, this is the only supported spell checker. |
|
|
| ## Installation |
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| 1. For CPU-only inference, install the CPU version of [PyTorch](https://pytorch.org/get-started/locally/). |
| 2. Make sure you can compile Python extension modules (required for KenLM). If you are on Linux, you can install them like this: |
| ``` |
| sudo apt-get install python-dev |
| ``` |
| 3. Install Speliuk: |
| ``` |
| pip install speliuk |
| ``` |
|
|
| ## Usage |
|
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| By default, Speliuk will use pre-trained models stored on [Hugging Face](https://huggingface.co/BonySmoke/Speliuk/tree/main). |
|
|
| ```python |
| >>> from speliuk.correct import Speliuk |
| >>> speliuk = Speliuk() |
| >>> speliuk.load() |
| >>> speliuk.correct("то він моее це зраабити для меніе?") |
| Correction(corrected_text='то він може це зробити для мене?', annotations=[Annotation(start=7, end=11, source_text='моее', suggestions=['може'], meta={}), Annotation(start=15, end=23, source_text='зраабити', suggestions=['зробити'], meta={}), Annotation(start=28, end=33, source_text='меніе', suggestions=['мене'], meta={})]) |
| ``` |
|
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| Speliuk can also be used directly from a spaCy model: |
| ```python |
| >>> import spacy |
| >>> from speliuk.correct import CorrectionPipe |
| >>> nlp = spacy.blank('uk') |
| >>> nlp.add_pipe('speliuk', config=dict(spacy_spelling_model_path='/my/custom/model')) |
| >>> doc = nlp("то він моее це зраабити для меніе?") |
| >>> doc._.speliuk_corrected |
| 'то він може це зробити для мене?' |
| >>> doc.spans["speliuk_errors"] |
| [моее, зраабити, меніе] |
| ``` |
|
|
| ## Training Details |
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| ### Spelling Error Detection |
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| To detect spelling errors, a spaCy NER model is used. |
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| It was trained on a combination of synthetic and golden data: |
| - For synthetic data generation, we used [UberText](https://lang.org.ua/en/ubertext/) as base texts and [nlpaug](https://github.com/makcedward/nlpaug) for errors generation. In total, 10k samples from different categories were used. |
| - For golden data, we used spelling errors from the [UA-GEC](https://github.com/grammarly/ua-gec) corpus. |
|
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| ### Perplexity Calculation |
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| We used KenLM for quick perplexity calculation. We used an existing model [Yehor/kenlm-uk](https://huggingface.co/Yehor/kenlm-uk) trained on UberText. |
|
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| ### Spell Checker |
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| We used [SymSpell](https://github.com/wolfgarbe/SymSpell) for error correction. The dictionary consists of 500k most frequent words from the UberText corpus. |
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