SinaTools是开源阿拉伯语NLP工具包，性能全面超越同类工具。
SinaTools: Open Source Toolkit for Arabic Natural Language Processing
- 整合多种阿拉伯语处理功能的统一工具包
- 在平铺/嵌套命名实体识别等任务上表现优异，最高达89.42%准确率
- 适合研究者与开发者快速集成到阿拉伯语自然语言系统中
我们介绍SinaTools，一个用于阿拉伯语自然语言处理与理解的开源Python工具包。SinaTools是一个统一的包，可轻松集成到系统工作流中，支持多种任务，包括平铺和嵌套命名实体识别（NER）、全标注词义消歧（WSD）、语义相关性、同义词抽取与评估、词形还原、词性标注、词根标注，以及语料库处理、文本清洗和带符号词匹配等辅助工具。本文介绍了SinaTools及其基准测试结果，表明其在上述任务中均优于现有工具，如平铺NER（87.33%）、嵌套NER（89.42%）、WSD（82.63%）、语义相关性（0.49斯皮尔曼等级相关）、词形还原（90.5%）、词性标注（97.5%）等。SinaTools可从https://sina.birzeit.edu/sinatools下载。
原文摘要 · Abstract (English)
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).
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