Arabic Name Entity Recognition Using Deep Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-09-19

AUTHORS

David Awad , Caroline Sabty , Mohamed Elmahdy , Slim Abdennadher

ABSTRACT

Many applications that we use on a daily basis incorporate Natural Language Processing (NLP), from simple tasks such as automatic text correction to speech recognition. A lot of research has been done on NLP for the English language but not much attention was given to the NLP of the Arabic language. The purpose of this work is to implement a tagging model for Arabic Name Entity Recognition which is an important information extraction task in NLP. It serves as a building block for more advanced tasks. We developed a deep learning model that consists of Bidirectional Long Short Term Memory and Conditional Random Field with the addition of different network layers such as Word Embedding, Convolutional Neural Network, and Character Embedding. Hyperparameters have been tuned to maximize the F1-score. More... »

PAGES

105-116

Book

TITLE

Statistical Language and Speech Processing

ISBN

978-3-030-00809-3
978-3-030-00810-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-00810-9_10

DOI

http://dx.doi.org/10.1007/978-3-030-00810-9_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107093797


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