1. The Named Entity Recognition (NER)
• Al-Shehri ,Aisha
• Almutairi ,Shaikhah
• Alswelim ,Haya
KINGDOM OF SAUDI ARABIA
Ministry of Higher Education
Al-Imam Muhammad Ibn Saud Islamic
University
College of Computer and Information Sciences

2. Abstract
Name Entity Recognition is an important part of many natural
language processing tasks .
There are different type of name entity such as people ,
location and organization .

3. Introduction
• The Named Entity Recognition is the identification and
classification of Named Entities within an open-domain text.
• The task of named entity recognition was defined as three
subtasks:
• ENAMEX.
• TIMEX, and NUMEX.

4. • We present the attempt at the recognition and
extraction of the most important proper name entity, that is,
the person name, for the Arabic language(PERA).
Components of an Arabic Full Name:
divided into five main categories, Ibn Auda (2003):
1. An ism (pronounced IZM).
2. A kunya (pronounced COON-yah).
3. By a nasab (pronounced NAH-sahb).
4. A laqab (pronounced LAH-kahb).
5. A nisba (pronounced NISS-bah).

5. Methodology
1-Parallel Corpora .
a-Reliability
b-Representativeness
2-Previously developed tools for other languages .
a-Person names
b-Location names (Geographical locations and Toponyms)
c-Organizations (Political of Administrative Entities)
d-Position (job titles)
e-Acronyms

6. Challenges
• 1- There is no capital letters or a specific signal in the
orthography like many other language.
• 2-The Arabic has different meaning
• 3-Abiguity

7. Ambiguous example
example CorrectIncorrectEnglish
translation
Ambiguous example
DatePerson15th of
Ramadan Al
karim 2005
CompanyLocationSaudi Aramco

8. Features
• Machine-learning features Word-Length.
• Noun-Flag
• Speech-Tag
• Type-Current
• Type-Left.
• Type-Right.

9. SystemArchitectureand Implementation
• Architecture of the NERA System:

10. SystemArchitectureand Implementation
• Gazetteers.
• Grammar.
• Filter.

11. SystemArchitectureand Implementation
1)Gazetteers:
Gazetteer containing: lists of known named entities.
White list:
The White list plays the role of fixed static dictionaries of
various NE.

12. SystemArchitectureand Implementation
2) Grammar:
The grammar performs recognition and extraction of Arabic
named entities from the input text based on derived rules.
The following are examples of indicators used within rules:
• Job title: (the doctor), (the sciences
professor).
• Person title: (Mr.) , (Mrs.).

13. SystemArchitectureand Implementation
3) Filter:
filter rules hels in dealing with recognition
ambiguity between named entities.
filtration mechanism is used that serves two different
purposes:revision of the NE extractor results and
disambiguation
of matches returned by different NE extractors.

14. Example:
variation
Typographic
Entity typeEnglish
translation
Arabic
example
Two dots removed from taa
marbouta
LocationSaudi
Arabia
Drop of the letter madda from the
aleph
LocationAsia

15. The Experiment

16. Results

17. Conclusion
• 1-We tried in the majority of cases to follow more general
criteria, applicable on English-Arabic transliteration or
French-Arabic transliteration.
• 2-This work is part of a new system for Arabic NER. It has
several ongoing activities.

18. References
• Sherief Abdallah, Khaled Shaalan, and Muhammad Shoaib ,
Integrating Rule-Based System with Classification for Arabic
Named Entity Recognition, 2012
• Yassine Benajiba , Mona Diab , and Paolo Rosso ,Using
Language Independent and Language Specific Features to
Enhance Arabic Named Entity Recognition, 2009
• Yassine Benajiba , Mona Diab , and Paolo Rosso , Arabic
Named Entity Recognition: AN SVM-BASED APPROACH, 2009
• Doaa Samy, Antonio Moreno, and José Mª Guirao, A Proposal
For An Arabic Named Entity Tagger Leveraging aParallel
Corpus,2005
• Khaled Shaalan, Hafsa Raza, Person Name Entity Recognition
for Arabic,2009