Improving coverage of rule based NER systems

Abstract

Named entity recognition (NER) is the problem of identifying (locating and categorizing) atomic entities in a given text that fall into predefined categories or classes. In this work, we developed a bilingual Arabic-English lexicon of named entities (NE) to improve the performance of Arabic rule-based systems. To reach our goal, we followed different steps starting by the pre-editing of the DBpedia linked data entities and the parallel corpus and then applying our automatic model for detection, extraction and translation of Arabic-English Named Entities. Our approach is fully automatic and hybrid, it combines linguistic and statistical methods.