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A semi-supervised learning approach to arabic named entity recognition

Althobaiti, M and Kruschwitz, U and Poesio, M (2013) A semi-supervised learning approach to arabic named entity recognition. In: UNSPECIFIED, ? - ?.


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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 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).

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: International Conference Recent Advances in Natural Language Processing, RANLP
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Sep 2015 16:54
Last Modified: 08 Jun 2022 00:04

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