Integrated Machine Learning Techniques for Arabic Named Entity Recognition
Named Entity Recognition (NER) task has become essential to
improve the performance of many NLP tasks. Its aim is to
endeavor a solution to boost accurately the identification of
extracted named entities. This paper presents a novel solution for
Arabic Named Entity Recognition (ANER) problem. The
solution is an integration approach between two machine
learning techniques, namely bootstrapping semi-supervised
pattern recognition and Conditional Random Fields (CRF)
classifier as a supervised technique. The paper solution
contributions are the exploit of pattern and word semantic fields
as CRF features, the adventure of utilizing bootstrapping semisupervised
pattern recognition technique in Arabic Language,
and the integration success to improve the performance of its
components. Moreover, as per to our knowledge, this proposed
integration has not been utilized for NER task of other natural
languages. Using 6-fold cross-validation experimental tests, the
solution is proved that it outperforms previous CRF sole work
and LingPipe tool.
Keywords: Bootstrapping Pattern Recognition, Conditional
Random Fields, Arabic Named Recognition, Cross-Validation
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