id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_2sn25vxvsrbzrjcp6ps7nehcte	Maha Althobaiti	Combining Minimally-supervised Methods for Arabic Named Entity Recognition	2015	14	.pdf	application/pdf	8201	875	61	Combining Minimally-supervised Methods for Arabic Named Entity Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations classifier and another one using distant learning techniques, and then combined them using amount of annotated data is available for many languages, including Arabic (Zaghouani, 2014), changing the domain or expanding the set of classes always requires domain-specific experts and new annotated data, both of which demand time and effort. we report our results from testing the recently proposed Independent Bayesian Classifier Combination Saha and Ekbal (2013) studied classifier combination techniques for various NER models under order to automatically develop an Arabic NE annotated corpus, which is used later to train a state-ofthe-art supervised classifier. The SSL classifier performs better than distant learning in detecting NEs minimal supervision approaches using various classifier combination methods leads to better results for Named entity recognition through classifier combination.	./cache/work_2sn25vxvsrbzrjcp6ps7nehcte.pdf	./txt/work_2sn25vxvsrbzrjcp6ps7nehcte.txt