A Semi-supervised Learning Approach to Arabic Named Entity Recognition
Maha Jarallah Althobaiti, Udo Kruschwitz, Massimo Poesio · Open Access at Essex (University of Essex) · 2013
We present ASemiNER, a semi-supervised algorithm for identifying Named Entities (NEs) in Arabic text. ASemiNER does not require annotated training data, or gazetteers. It also can be easily adapted to handle more than the three standard NE types (Person, Location, and Organisation). To our knowledge, our algorithm is the first study that intensively investigates the semi-supervised pattern-based learning approach to Arabic Named Entity Recog-nition (NER). We describe ASemiNER and compare its performance with dif-ferent supervised systems. We evaluate this algorithm by way of experiments to extract the three standard named-entity types. Ultimately, our algorithm out-performs simple supervised systems and also performs well when we evaluate its performance in order to extract three new, specialised types of NEs (Politicians, Sportspersons, and Artists). 1