SinaTools: Open Source Toolkit for Arabic Natural Language Processing
This toolkit addresses the need for comprehensive, high-performance NLP tools for Arabic, a domain-specific language, though it is incremental as it builds on existing methods.
The authors introduced SinaTools, an open-source Python toolkit for Arabic natural language processing, which outperforms existing tools on tasks like Named Entity Recognition (e.g., 87.33% for flat NER) and Part-of-speech Tagging (97.5%).
We introduce SinaTools, an open-source Python package for Arabic natural language processing and understanding. SinaTools is a unified package allowing people to integrate it into their system workflow, offering solutions for various tasks such as flat and nested Named Entity Recognition (NER), fully-flagged Word Sense Disambiguation (WSD), Semantic Relatedness, Synonymy Extractions and Evaluation, Lemmatization, Part-of-speech Tagging, Root Tagging, and additional helper utilities such as corpus processing, text stripping methods, and diacritic-aware word matching. This paper presents SinaTools and its benchmarking results, demonstrating that SinaTools outperforms all similar tools on the aforementioned tasks, such as Flat NER (87.33%), Nested NER (89.42%), WSD (82.63%), Semantic Relatedness (0.49 Spearman rank), Lemmatization (90.5%), POS tagging (97.5%), among others. SinaTools can be downloaded from (https://sina.birzeit.edu/sinatools).