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--- |
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license: cc-by-sa-4.0 |
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datasets: |
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- cjvt/cc_gigafida |
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- cjvt/solar3 |
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- cjvt/sloleks |
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language: |
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- sl |
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tags: |
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- word spelling error annotator |
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--- |
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--- |
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language: |
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- sl |
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license: cc-by-sa-4.0 |
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--- |
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# SloBERTa-Incorrect-Spelling-Annotator |
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This SloBERTa model is designed to annotate incorrectly spelled words in text. It utilizes the following labels: |
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- 1: Indicates incorrectly spelled words. |
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- 2: Denotes cases where two words should be written together. |
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- 3: Suggests that a word should be written separately. |
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## Model Output Example |
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Imagine we have the following Slovenian text: |
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_Model vbesedilu o znači besede, v katerih se najajajo napake._ |
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If we convert input data to format acceptable by SloBERTa model: |
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_Model <mask> vbesedilu <mask> o <mask> znači <mask> besede <mask> , <mask> v <mask> katerih <mask> se <mask> najajajo <mask> napake <mask> . <mask>_ |
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The model might return the following predictions (note: predictions chosen for demonstration/explanation, not reproducibility!): |
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_Model 0 vbesedilu 3 o 2 znači 2 besede 0 , 0 v 0 katerih 0 se 0 najajajo 1 napake 0 . 0_ |
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We can observe the following: |
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1. In the input sentence, the word `najajajo` is spelled incorrectly, so the model marks it with the token (0). |
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2. The word `vbesedilu` should be written as two words `v` and `besedilu`, so the model marks it with the token (3). |
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3. The words `o` and `znači` should be written as one word `označi`, so the model marks them with the tokens (2). |
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## More details |
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The model, along with its training and evaluation, is described in more detail in the following paper. |
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``` |
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@inproceedings{neural-spell-checker, |
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author = {Klemen, Matej and Bo\v{z}i\v{c}, Martin and Holdt, \v{S}pela Arhar and Robnik-\v{S}ikonja, Marko}, |
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title = {Neural Spell-Checker: Beyond Words with Synthetic Data Generation}, |
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year = {2024}, |
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doi = {10.1007/978-3-031-70563-2_7}, |
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booktitle = {Text, Speech, and Dialogue: 27th International Conference, TSD 2024, Brno, Czech Republic, September 9–13, 2024, Proceedings, Part I}, |
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pages = {85–96}, |
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numpages = {12} |
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} |
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``` |
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## Acknowledgement |
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The authors acknowledge the financial support from the Slovenian Research and Innovation Agency - research core funding No. P6-0411: Language Resources and Technologies for Slovene and research project No. J7-3159: Empirical foundations for digitally-supported development of writing skills. |
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## Authors |
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Thanks to Martin Božič, Marko Robnik-Šikonja and Špela Arhar Holdt for developing these models. |
