Paper Info
Title
Named Entity Recognition on Arabic-English Code-Mixed Data
Abstract
As a result of globalization and better quality of education, a significant percentage of the population in Arab countries have become bilingual/multilingual. This has raised to the frequency of code-switching and code-mixing among Arabs in daily communication. Consequently, huge amount of Code-Mixed (CM) content can be found on different social media platforms. Such data could be analyzed and used in different Natural Language Processing (NLP) tasks to tackle the challenges emerging due to this multilingual phenomenon. Named Entity Recognition (NER) is one of the major tasks for several NLP systems. It is the process of identifying named entities in text. However, there is a lack of annotated CM data and resources for such task. This work aims at collecting and building the first annotated CM Arabic-English corpus for NER. Furthermore, we constructed a baseline NER system using deep neural networks and word embedding for Arabic-English CM text and enhanced it using a pooling technique.
Year
DOI
Venue
2019
10.1109/ICOSC.2019.8665500
2019 IEEE 13th International Conference on Semantic Computing (ICSC)
Keywords
Field
DocType
Task analysis,Hidden Markov models,Natural language processing,Twitter,Neural networks,Support vector machines
Population,Social media,Task analysis,Computer science,Pooling,Natural language processing,Artificial intelligence,Word embedding,Hidden Markov model,Artificial neural network,Named-entity recognition
Conference
ISSN
ISBN
Citations 
2325-6516
978-1-5386-6783-5
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Caroline Sabty111.72
Mohamed Elmahdy2134.57
Slim Abdennadher339460.95