Overview

About

SinaTools: Open Source Toolkit for Arabic Natural Language Understanding. It's a Python-based toolkit developed to facilitate Arabic Natural Language Understanding (NLU). The toolkit encompasses various modules tailored to address key NLU tasks, including Named Entity Recognition (NER), Word Sense Disambiguation (WSD), Semantic Relatedness, Synonymy Extractions, Lemmatization, Part-of-speech (POS) Tagging and additional helper utilities such as corpus processing, striping methods, and Diacritic-Based Matching of Arabic Words.
SinaTools leverages state-of-the-art models and datasets to enhance the accuracy and efficiency of NLU tasks in Arabic. For instance, the NER module is fine-tuned using the Wojood dataset and a BERT model, supporting flat, nested, and fine-grained entity types. The Semantic Relatedness module utilizes cosine similarity to evaluate the association between sentence pairs, outperforming baselines in the SemEval-2024 Task. Additionally, the Synonyms Module employs a sophisticated algorithm to extract synonyms from multilingual dictionaries and thesauri, enhancing the toolkit's capabilities in semantic analysis.
SinaTools aims to democratize Arabic NLU by providing accessible and efficient solutions for a wide range of NLU tasks, catering to both experts and non-experts in the field. The toolkit's open-source nature and comprehensive documentation further contribute to its usability and adoption in the Arabic NLP community.
In conclusion, SinaTools emerges as a valuable resource for researchers, developers, and practitioners seeking robust and user-friendly tools for Arabic NLU.

For more details on downloading SinaTools and getting started, please refer to the Getting Started section.

SinaTools is available under the MIT license. See License for more information.