Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology

Name entity recognition (NER) has emerged as a natural language processing (NLP) technology that is effective and can provide high value to several different kinds of application such as Information extraction (IE), Information retrieval (IR),Text to speech, question answering (QA), text clustering, etc. NER is responsible for the identification of proper names in text and their classification as different types of named entity such as people, locations, and organizations. Arabic language imposes added challenges for that task. In this thesis we presented a new approach to enhance the solution of the problem of Arabic name entity recognition (ANER). The introduced approach uses different sets of features that are both language independent and language specific in a discriminative and generative machine learning frameworks namely, conditional random fields (CRF) and support vector machines (SVM), Naive Bayes (NB), Decision Tree (DT), SVM for sequence tagging using Hidden Markov Models (SVMhmm), K - nearest neighbors(K-NN), Logistic classifier and the other SVM Weka implementation called (SMO). These classifiers have been fused together using one of the following methods: Majority voting,

Issued also as CD