Abstract
This study attempts to describe and discuss the different approaches and methods dedicated to Named Entity Recognition (NER) systems in various languages, in order to justify the choice of a distributional approach for an Arabic NER system using deep learning methods and a Neural Network word representation (Embeddings) as an add-in feature in the unsupervised learning process.
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Notes
- 1.
- 2.
Their dictionary was created by using Encyclopedia.
- 3.
Entropy is a measure of uncertainty.
- 4.
The BIO method stand for: the Beginning, the inside and the Outside of the Entity.
- 5.
ANERsys is the name of the Arabic NER system created by BENAJIBA and his team of researchers, it’s available at: http://users.dsic.upv.es/~ybenajiba.
- 6.
Recall is an evaluation measure, used for NLP application.
- 7.
A tool created by Mikolov, it’s a group of related models used to create word Embeddings <https://github.com/dav/word2vec>.
- 8.
Stochastic Gradient Descent code <https://github.com/mateuszmalinowski/SGD>.
- 9.
Back-Propagation Net <https://backpropagation-neural-network.soft112.com/>.
- 10.
Natural Language toolkit is a leading platform for building Python programs to work with human language data <http://www.nltk.org>.
- 11.
An integrated development environment for text engineering <https://gate.ac.uk/>.
- 12.
crawler for effective creation and annotation of linguistic corpora < http://corpus.tools/browser/spiderling>.
- 13.
A heuristic based boilerplate removal tool <http://code.google.com/p/justext/>.
- 14.
A de-duplication tool <https://code.google.com/p/onion>.
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Azroumahli, C., El Younoussi, Y., Achbal, F. (2018). An Overview of a Distributional Word Representation for an Arabic Named Entity Recognition System. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_13
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