The extraction of named entities from unstructured text is a crucial component in numerous Natural LanguageProcessing (NLP) applications such as information retrieval, question answering, machine translation, to namebut a few. Named-entity Recognition (NER) aims at locating proper nouns from unstructured text and clas-sifying them into a predefined set of types, such as persons, locations, and organizations. There has beenextensive research on improving the accuracy of NER in English text. For other languages such as Arabic,extracting Named-entities is quite challenging due to its morphological structure. In this paper, we introduceArabiaNer, a system employing Conditional Random Field (CRF) learning algorithm with extensive featureengineering steps to effectively extract Arabic named Entities.ArabiaNerproduced state-of-the-art resultswith f1-score of 91.31% when applied on the ANERcrop dataset.

عنوان المؤتمر

The 13th International Conference on Agents and Artificial Intelligence (ICAART)

دولة المؤتمر

البرتغال

تاريخ المؤتمر

2 فبراير، 2021 - 4 فبراير، 2021

راعي المؤتمر

https://portal.insticc.org/

معلومات إضافية

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