We present ASemiNER, a semisupervised 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 Recognition (NER). We describe ASemiNER and compare its performance with different supervised systems. We evaluate this algorithm by way of experiments to extract the three standard named-entity types. Ultimately, our algorithm outperforms 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).
Semi-supervised learning was used in REF to develop ASemiNER, a semisupervised algorithm for identifying Named Entities (NEs).
A Semi-supervised Learning Approach to Arabic Named Entity Recognition
RANLP
Computer Science