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