An approach for extracting and disambiguating arabic persons' names using clustered dictionaries and scored patterns

Building a system to extract Arabic named entities is a complex task due to the ambiguity and structure of Arabic text. Previous approaches that have tackled the problem of Arabic named entity recognition relied heavily on Arabic parsers and taggers combined with a huge set of gazetteers and sometimes large training sets to solve the ambiguity problem. But while these approaches are applicable to modern standard Arabic (MSA) text, they cannot handle colloquial Arabic. With the rapid increase in online social media usage by Arabic speakers, it is important to build an Arabic named entity recognition system that deals with both colloquial Arabic and MSA text. This paper introduces an approach for extracting Arabic persons' name without utilizing any Arabic parsers or taggers. Evaluation of the presented approach shows that it achieves high precision and an acceptable level of recall on a benchmark dataset.