Fine-grained Arabic named entity recognition

Named Entity Recognition (NER) is a Natural Language Processing (NLP) task, which aims to extract useful information from unstructured textual data by detecting and classifying Named Entity (NE) phrases into predefined semantic classes. This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly inflection process. Instead of classifying the detected NE phrases into small sets of classes (i.e. coarsegrained ranged from 3 to 10); we target a broader range (i.e. 50 fine-grained classes ‘hierarchal-based of two levels’) to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in the number of semantic classes and the decrease in semantic differences between fine-grained classes. Fine-grained NER is advantageous in various NLP tasks, including Information Extraction, Ontology Construction and Populations, and Question Answering among many others. Our approach to developing fine-grained NER relies on two different supervised Machine Learning (ML) technologies (i.e. Maximum Entropy ‘ME’ and Conditional Random Fields ‘CRF’), which require annotated (i.e. labelled) training data (i.e. a corpus) in order to learn by extracting informative features. Therefore, the development of such resources comprises one of the thesis contributions. We develop a methodology which exploit the richness of Arabic Wikipedia (AW) in order to create a scalable fine-grained lexical resource (gazetteer) and a corpus automatically. Moreover, two gold-standard cre-