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license: mit |
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# CAMeLBERT-CATiB-parser |
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## Model description |
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The **CAMeLBERT-CATiB-parser** is a neural dependency parsing model for Arabic text, specifically designed for the CATiB dependency formalism. |
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It is based on the Biaffine Attention Dependency Parsing model introduced by [Dozat and Manning (2017)](https://arxiv.org/pdf/1611.01734.pdf) and implemented in |
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[SuPar](https://github.com/yzhangcs/parser), which has been shown to be very effective for dependency parsing in many languages. |
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The model is trained on the CamelTB and PATB combined train sets, which are both large Arabic corpora. |
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The model uses a CamelBERT-MSA word embedding layer, which is a pre-trained language model that has been trained on a massive dataset of Arabic text. |
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The model was introduced in our paper "CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic". |
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The paper describes the model in detail and evaluates its performance on various Arabic dependency parsing tasks. |
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## Intended uses |
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The CAMeLBERT-CATiB-parser is shipped with the [CAMeLParser](https://github.com/CAMeL-Lab/camel_parser) as one of the default parsing models, |
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and can be selected when parsing texts using the CATiB formalism. |
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## Citation |
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```bibtex |
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@inproceedings{Elshabrawy:2023:camelparser, |
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title = "{CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic}", |
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author = {Ahmed Elshabrawy and |
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Muhammed AbuOdeh and |
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Go Inoue and |
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Nizar Habash} , |
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booktitle = {Proceedings of The First Arabic Natural Language Processing Conference (ArabicNLP 2023)}, |
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year = "2023" |
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} |
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``` |
