New Combined Method to Improve Arabic POS Tagging

• 作者: Mohamed Labidi
• 来源期刊: 自主智能(英文)
• 学科: 工学
• 关键词: POS-Tagger NATURAL LANGUAGE processing ARABIC LANGUAGE
• 年，卷（期）: 2018,（2）
• 页码范围: 23-28
• 页数: 6页
• 分类号: TP

摘要

One of the important tasks in Natural language processing is the part of speech tagging. For the Arabic language we have a lot of works but their performances do not rise to the required level, due to the complexity of the task and the Arabic language characteristics. In this work we study a combination between two different approaches for Arabic POSTaggers. The first one is a maximum entropy-based one, and the second is a statistical/rule-based one. Fur-thermore, we add a knowledge-based method to annotate Arabic particles. Our idea improves the accuracy rate. We passed from almost 85% to almost 90% using our combined method, which seem promoter.