Slides for 'JOSA TechTalks: Arabic NLP in Practice' with an introduction to Natural Language Understanding (NLU) with focus on Arabic, covers main issues in (Arabic) NLU and used tools and resources. By Dr. Samir Tartir - Senior Research Scientist of AI at Mawdoo3

1. Processing Arabic Text
JOSA TechTalk
Dr. Samir Tartir
1/May/2019
Processing Arabic Text

2. Outline
 Intros
 NLU
 Issues in NLU
 Issues in Arabic NLU
 Tools & Resources

3. Dr. Samir Tartir
 Senior Research Scientist of AI at Mawdoo3
 PhD from the University of Georgia (USA) in 2009
 BSc & MSc from the University of Jordan in 1998 & 2002
 Highly referenced researcher
 Recognized educator (CourseHero.com)
 Research areas:
 Arabic NLP, Semantic Web, & Ontologies.
 Previously:
 Head and founder of the Department of Web Engineering at Philadelphia University (1 year)
 Assistant professor at the Department of Computer Science at Philadelphia University (9 years)
 Google outsourced team leader 2011-2016
 Database programmer for 5 years (in Jordan, USA)

5. Mawdoo3 is the
largest Arabic
website in the world.
Committed to
enhance the Arabic
language through
content and
technology
Mawdoo3 © Copyright 2018. All Rights Reserved.

6. ACHIEVEMENTS
Mawdoo3 © Copyright 2018. All Rights Reserved.
50Mmonthly active
users (MAU)
150Karticles
300paid
experts
contributors
100Bbillion words
read every
month
GENERAL

7. Mawdoo3 AI
 Founded in January 2017
 Leverage data collected by Mawdoo3.com for AI
 Currently working on Salma
 The first Arabic Voice Enabled Assistant
 The beta version was released during a Silicon Valley event
 We are about to release the next version with major updates
(weekend)
 It is available for download for Android and iOS devices
 Continuously improving (you’ll understand soon)

8. Salma Introduction

10. http://www.cellstrat.com/2017/10/27/nlp-vs-nlu-vs-nlg/

11. https://labs.eleks.com/2018/02/how-to-build-nlp-engine-that-wont-screw-
up.html

12. blah blah
blah
?!?!?!?!

13. blah blah
blah
5 words: I,
must,
organize, my,
room

14. blah blah
blah
I must
organize
my room
?!?!?!?!

15. blah blah
blah
I must
organize
my room

16. Many issues*
1. Easy or mostly solved issues
2. Issues with Intermediate progress
3. Hard Issues or still need lot of work
Mostly applies to English, but also applies to Arabic
*https://www.quora.com/What-are-the-major-open-problems-in-natural-language-understanding

17. 1. Easy or mostly solved
 Spam detection and email classification
 Works!

18. 1. Easy or mostly solved
 Part of Speech Tagging (≈ ‫)إعراب‬
 Follows tokenization (‫تجزيء‬)
 KEY: N = Noun, V = Verb, P = Preposition, Adv = Adverb, etc.
 INPUT:
Profits soared at Boeing Co., easily topping forecasts on Wall
Street, as their CEO Alan Mulally announced first quarter results.
 OUTPUT:
Profits/N soared/V at/P Boeing/N Co./N ,/, easily/ADV topping/V
forecasts/N on/P Wall/N Street/N ,/, as/P their/POSS CEO/N
Alan/N Mulally/N announced/V first/ADJ quarter/N results/N ./.

19. 1. Easy or mostly solved
 Named Entity Recognition
 Usual classes: People, organizations, locations
 KEY: P = Person, O = Organization, L = Location
 INPUT:
Profits soared at Boeing Co., easily topping forecasts on Wall
Street, as their CEO Alan Mulally announced first quarter results.
 OUTPUT:
Profits soared at {Boeing Co.}/O , easily topping forecasts on {Wall
Street}/L , as their CEO {Alan Mulally}/P announced first quarter
results .

20. ‫أبدى‬ ‫وقد‬‫سوالنا‬ ‫خافيير‬‫اال‬ ‫في‬ ‫الخارجية‬ ‫للسياسة‬ ‫األعلى‬ ‫المنسق‬‫تحاد‬
‫األوروبي‬،‫المصرية‬ ‫بالمبادرة‬ ‫كبيرا‬ ‫تفاؤال‬،‫ا‬ ‫إطالق‬ ‫وقف‬ ‫احتمال‬ ‫مرجحا‬‫لنار‬
،‫قريبا‬‫من‬ ‫اإلسرائيلية‬ ‫القوات‬ ‫انسحاب‬ ‫فرنسية‬ ‫مصادر‬ ‫وتوقعت‬‫غزة‬‫خالل‬
،‫أيام‬ ‫ثمانية‬‫وأوضح‬‫سوالنا‬‫دعوة‬ ‫أن‬‫مصر‬‫إل‬‫سرائيل‬‫ق‬ ‫الهجوم‬ ‫وقف‬ ‫لبحث‬‫د‬
‫المقبلة‬ ‫القليلة‬ ‫الساعات‬ ‫خالل‬ ‫ثمارها‬ ‫تؤتي‬،‫الع‬ ‫دائمة‬ ‫الدول‬ ‫أن‬ ‫مؤكدا‬‫ضوية‬
‫األمن‬ ‫بمجلس‬‫شديد‬ ‫بترحاب‬ ‫المصرية‬ ‫المبادرة‬ ‫استقبلت‬.
Named Entities:
LocationsPersons
OrganizationsArabic NER ‫األعالم‬ ‫مستخرج‬
Example
20
*Microsoft Arabic NLP Toolkit (ATK) For Academia in the Arab World Presentation, 11/2012

21. 1. Easy or mostly solved
 Plagiarism detection
 INPUT:
A research paper, homework report, etc.
 OUTPUT:
Rate of similarity to other papers

22. 2. Intermediate or making good
progress
 Sentiment analysis - Examples:
 Best roast chicken in San Francisco! – Positive
 The waiter ignored us for 20 minutes. – Negative
 ‫قال‬ ‫مطعم‬ ‫احسن‬ ‫قال‬!

23. 2. Intermediate or making good
progress
 Coreference resolution - Example:
 "The thieves stole the paintings. They were subsequently caught.“
 "The thieves stole the paintings. They were subsequently sold.“
 "The thieves stole the paintings. They were subsequently found.“
 To solve whether “they" is related to “the thieves" or “the
paintings".

24. 2. Intermediate or making good
progress
 Word sense disambiguation - Example:
 “I need new batteries for my mouse”

25. 2. Intermediate or making good
progress
 Machine Translation
 Translating sentences from one language to another
 “Best” example would be Google translate.

26. 3. Hard or still need lot of work
 Text Summarization
 to take input as text document(s) and try to condense them into a
summary.
https://www.springer.com/us/book/9783030167998
© 2019
Lithium-Ion Batteries
A Machine-Generated Summary of Current Research
Authors: Writer, Beta
•The first machine-generated book in chemistry
•Provides an overview of recent research
•Includes summaries of 150 articles

27. 3. Hard or still need lot of work
 Machine dialog system - Example:
 User - I need a flight from New York to London, arriving at 10 pm ?
 System - What day are you leaving?
 User - Tomorrow.
The system detected the missing information in your sentences.

28. Arabic

29. Background
 History
– Arabic has remained intelligible and functional for around 20
centuries.
 Strategically important
– 330 million speakers living in an important region
 huge oil reserves, sacred sites, sea ports
– 1.4 billion Muslims use in their prayers.
 Cultural and literary heritage
– Closely associated with Islam

30. Distribution

31. ‫ثمن‬

32. ‫علم‬

33. ‫ق‬

34. One-Letter Verbs*
.1(‫إ‬)‫من‬‫ى‬َ‫َأ‬‫و‬‫ي‬‫َئ‬‫ي‬‫ا‬ً‫ي‬ْ‫َأ‬‫و‬,‫والوأي‬:‫الوعد‬.
.2(‫ت‬)‫ا‬ًّ‫ي‬‫ت‬
ُ
‫وأ‬ ‫ا‬ً‫ي‬ْ‫ت‬َ‫أ‬ ‫ي‬‫ت‬ْ‫َأ‬‫ي‬ ‫ى‬َ‫ت‬َ‫أ‬ ‫من‬‫ت‬‫وإ‬‫ا‬ًّ‫ي‬‫ا‬ً‫ن‬‫َا‬‫ي‬ْ‫ت‬‫وإ‬َ‫ن‬‫َا‬‫ي‬ْ‫ت‬‫وإ‬ً‫ة‬‫؛‬ً‫ة‬‫ا‬َ‫ت‬ْ‫َأ‬‫م‬‫و‬
‫أي‬:‫جئته‬,‫األمر‬ ‫في‬ ‫تقول‬ ‫العرب‬ ‫وبعض‬(:‫ائت‬),‫وأك‬‫ثر‬
‫بـ‬ ‫يأمرون‬ ‫الناس‬(‫ائت‬)‫ال‬(‫ت‬.!)
.3(‫ث‬)‫أي‬ ‫َثي؛‬‫ي‬ ‫ى‬َ‫ث‬َ‫و‬ ‫من‬:‫السل‬ ‫عند‬ ‫به‬ ‫ى‬ َ‫َش‬‫و‬‫طان‬.
.4(‫ج‬)‫قطع‬ ‫أي‬ ‫َجي؛‬‫ي‬ ‫َى‬‫ج‬َ‫و‬ ‫من‬,‫وأما‬:‫َجا‬‫و‬‫ال‬‫الح‬ ‫فهو‬‫فا‬.
.5(‫ح‬)‫ا‬ً‫ي‬ْ‫َح‬‫و‬ ‫َحي‬‫ي‬ ‫َى‬‫ح‬َ‫و‬ ‫من‬,‫والوحي‬:‫وال‬ ‫اإلشارة‬‫كتابة‬
‫والكالم‬.
.6(‫خ‬)‫أي‬ ‫ا؛‬ً‫ي‬ْ‫َخ‬‫و‬ ‫ي‬‫َخ‬‫ي‬ ‫ى‬َ‫خ‬َ‫و‬:‫قصد‬.
.7(‫د‬)‫أي‬ ‫ا؛‬ً‫ي‬ْ‫د‬َ‫و‬ ‫ي‬‫َد‬‫ي‬ ‫َى‬‫د‬َ‫و‬ ‫من‬:‫الدية‬ ‫دفع‬.
.8(َ‫ر‬)‫أ‬-‫ا‬ً‫ي‬ْ‫أ‬َ‫ر‬‫و‬ ً‫ة‬‫ي‬ْ‫و‬ُ‫ر‬ َ‫ل‬‫الهال‬ ‫ى‬َ‫ر‬َ‫ي‬ ‫ى‬َ‫أ‬َ‫ر‬ ‫من‬‫و‬ً‫ة‬َ‫ء‬‫ا‬َ‫ر‬.
.9(َ‫ر‬)‫ب‬-‫ا‬ً‫ي‬ْ‫ر‬َ‫و‬ ‫ه‬َ‫ف‬‫جو‬ ُ‫القيح‬ ‫ي‬‫َر‬‫ي‬ ‫ى‬َ‫ر‬َ‫و‬ ‫ومن‬‫أي‬ ‫؛‬:‫أفسده‬.
.10(‫س‬)ً‫ي‬ ْ‫َس‬‫و‬ ‫عمرو‬ َ‫رأس‬ ٌ‫د‬‫زي‬ ‫َسي‬‫ي‬ ‫ى‬ َ‫َس‬‫و‬ ‫من‬‫أي‬ ‫ا؛‬
‫حلقه‬.
.11(‫ش‬)‫ا‬ً‫ي‬ ْ‫َش‬‫و‬ ‫ي‬‫َش‬‫ي‬ ‫ى‬ َ‫َش‬‫و‬ ‫من‬,‫والوشي‬:‫ن‬‫قش‬
‫الثوب‬.
.12(‫ص‬)ْ‫ص‬َ‫و‬ ‫بالشيء‬ َ‫ء‬‫الشي‬ ‫ي‬‫َص‬‫ي‬ ‫َى‬‫ص‬َ‫و‬ ‫من‬‫أي‬ ‫ا؛‬ً‫ي‬
‫وصله‬.
.13(‫ع‬)‫أي‬ ‫ا؛‬ً‫ي‬ ْ‫َع‬‫و‬ ‫ي‬‫َع‬‫ي‬ ‫ى‬َ‫َع‬‫و‬ ‫من‬:‫وجمع‬ ‫حفظ‬.
.14(‫ف‬)‫ا‬ ‫الوفاء‬ ‫بمعنى‬ ‫؛‬ً‫ء‬‫ا‬َ‫َف‬‫و‬ ‫ي‬‫َف‬‫ي‬ ‫ى‬َ‫َف‬‫و‬ ‫من‬‫لعهد‬.
.15(‫ق‬)َ‫و‬‫و‬ ً‫ة‬َ‫ي‬‫ا‬َ‫ق‬‫وو‬ ‫ا‬ً‫ي‬ْ‫َق‬‫و‬ ‫ي‬‫َق‬‫ي‬ ‫ى‬َ‫َق‬‫و‬ ‫من‬ً‫ة‬َ‫ي‬‫اق‬,‫بمعنى‬
‫الحفظ‬,‫مثلثة‬ ‫الوقاية‬ ‫واو‬.
.16(‫ك‬)َ‫ة‬‫ْب‬‫ر‬‫الق‬ ‫ي‬‫َك‬‫ي‬ ‫ى‬َ‫َك‬‫و‬ ‫من‬,‫والوكاء‬:‫ا‬ ‫رباط‬‫لقربة‬
‫وغيرها‬.
.17(‫ل‬)ً‫ة‬‫والي‬ ‫ي‬‫َل‬‫ي‬ ‫ى‬َ‫َل‬‫و‬ ‫من‬,‫والوالية‬:‫اإلمارة‬.
.18(‫م‬)‫أشار‬ ‫ا؛‬ً‫ي‬ْ‫م‬َ‫و‬ ‫ي‬‫َم‬‫ي‬ ‫َى‬‫م‬َ‫و‬.
.19(‫ن‬)ً‫ء‬‫نا‬‫وو‬ ‫ا‬ً‫ي‬ْ‫ن‬َ‫و‬ ‫ي‬‫َن‬‫ي‬ ‫ى‬َ‫ن‬َ‫و‬,‫أي‬:‫تعب‬.
.20(‫ـ‬‫ه‬)‫أي‬ ‫ا؛‬ً‫ي‬ْ‫َه‬‫و‬ ‫ي‬‫َه‬‫ي‬ ‫ى‬َ‫ه‬َ‫و‬:‫ضعف‬.
*https://www.m-a-arabia.com/vb/showthread.php?t=140

35. Versions
 Classical
 ‫مشكلة‬ ‫فصحى‬
 Modern Standard Arabic (MSA)
 ‫مشكلة‬ ‫غير‬ ‫فصحى‬ (diacritics)
 Dialects (‫)اللهجات‬
1. Egyptian Arabic (EGY)
2. Levantine Arabic (LEV)
3. Gulf Arabic (GLF)
4. North African Arabic (NOR)
 ‫كراء‬:‫استئجار‬

36. Arabic Language Characteristics
 Written to the left
 Highly structured
 Highly derivational language
 Morphology
 Free word order
 Many dialects
 Long sentences

37. Example*
*Microsoft Arabic NLP Toolkit (ATK) For Academia in the Arab World Presentation, 11/2012

38. Issues Specific to Arabic, Part 1
 No central entity that governs the language
 E.g. produces dictionaries
 Punctuation mark rules
 The large area covered imported different foreign words from different
neighboring languages
 Letters:
 One letter, one sound
 Letters change shape
 Hamza
 No capital letters
 High error rate of written text

39. High WER
 Arabic content has a very high Word Error Rate (WER).
 Analysis of 1000-Article Tagged Corpus: the Average WER is 6% in News text.
39
0
2
4
6
8
10
12
14 News Site Error Rate
Akbar El Youm 13 %
BBC Arabic 9 %
AL-Nahar 8 %
Al-Syassa 8 %
Al-Ahram 8 %
Al-Quds 8 %
Al-Qabas 5 %
Al-Hayat 4 %
Al-Jazeera 1 %
*Microsoft Arabic NLP Toolkit (ATK) For Academia in the Arab World Presentation, 11/2012

40. Error Class
Error
Rate
Missing Hamza 21%
Extra Hamza 19%
Missing Yaa 15%
Extra Yaa 12%
Missing TaaMarbouta 11%
Extra TaaMarbouta 8%
Wrong Hamza 7%
Missing Space 2%
Swapped Letters 1%
High WER
40
*Microsoft Arabic NLP Toolkit (ATK) For Academia in the Arab World Presentation, 11/2012
Hamza (47%) + Yaa (15%) + Taa (19%) = 81%

41. Issues Specific to Arabic, Part 1
 Synonymy and confusion of non-standardized terms
 Thermometer: ‫ترمومتر‬ ،‫حرارة‬ ‫ميزان‬ ،‫حرارة‬ ‫مقياس‬ ،‫محرار‬ ،‫محر‬
 Technical translation
 Hydrometer: ‫السوائل‬ ‫كثافة‬ ‫قياس‬ ‫جهاز‬
 Male/female forms
 Dual form
 Uncle, parent…

42. The Qadafi/Schwarzenegger Problem *
Arabic English
‫قذافي‬ Gadafi
Gaddafi
Gaddfi
Gadhafi
Ghaddafi
Kadaffy
Qaddafi
Qadhafi
‫شوارزنيغر‬
‫شوارزنغر‬
‫شوارزنيجر‬
‫شوارزنجر‬
Schwarzenegger
* Mona Diab & Nizar Habash. Natural Language Processing of Arabic and its Dialects. EMNLP 2014, Doha, Qatar.

43. Ambiguity
 Homographs
 ‫قدم‬
 Internal word structure ambiguity
 ‫بعقوبة‬
 Syntactic ambiguity
 ‫الجديد‬ ‫البنك‬ ‫مدير‬ ‫قابلت‬
 Semantic ambiguity
 ‫ابراهيم‬ ‫من‬ ‫اكثر‬ ‫احمد‬ ‫علي‬ ‫يحب‬
 Anaphoric ambiguity
 ‫انتقده‬ ‫الذي‬ ‫الوزير‬ ‫الصحفي‬ ‫قابل‬
• Homonyms: words that sound alike but
have different meanings.
• Homophones: homonyms that sound
alike and have different meanings, but
have different spellings.
• Homographs: words that are spelled
the same but have different meanings.

44. So?

45. Ambiguity in Word Segmentation*
* Freihat Abed Alhakim, et al. A Single A Single-Model Approach for Arabic Segmentation, POS-Tagging, and Named Tagging, and Named Entity
Recognition Entity Recognition Entity Recognition Entity Recognition.

46. Ambiguity in POS-Tagging*
* Freihat Abed Alhakim, et al. A Single A Single-Model Approach for Arabic Segmentation, POS-Tagging, and Named Tagging, and Named Entity
Recognition Entity Recognition Entity Recognition Entity Recognition.

47. Ambiguity in Fine-grained POS-Tagging*
* Freihat Abed Alhakim, et al. A Single A Single-Model Approach for Arabic Segmentation, POS-Tagging, and Named Tagging, and Named Entity
Recognition Entity Recognition Entity Recognition Entity Recognition.

48. NLP Issues that Apply to Arabic (and
many others)
 Lack of resources
 Financial (governmental and private)
 Human (computer and linguistic researchers and programmers)
 Lack of tools
 Lack of linguistic references
 E.g. lexical semantics (classes, synonyms, relationships)
 Lack of corpora
 Training data
 Benchmarks

49. It’s NOT
only
Arabic

50. English
 Hewlett-Packard → Hewlett and Packard as two tokens?
 state-of-the-art: break up hyphenated sequence.
 co-education
 lowercase, lower-case, lower case ?
 It can be effective to get the user to put in possible hyphens
 San Francisco: one token or two?
 How do you decide it is one token?
Sec. 2.2.1
C. D Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008

51. French
 L'ensemble → one token or two?
 L ? L’ ? Le ?
 Want l’ensemble to match with un ensemble
 Until at least 2003, it didn’t on Google
 Internationalization!
Sec. 2.2.1
C. D Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008

52. German
 Lebensversicherungsgesellschaftsangestellter
 ‘life insurance company employee’
 German retrieval systems benefit greatly from a compound
splitter module
 Can give a 15% performance boost for German
C. D Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008

53. Japanese
 Japanese (and Chinese) have no spaces between words:
 莎拉波娃现在居住在美国东南部的佛罗里达。
 Not always guaranteed a unique tokenization
 Further complicated in Japanese, with multiple alphabets
intermingled
 Dates/amounts in multiple formats
フォーチュン500社は情報不足のため時間あた$500K(約6,000万円)
Katakana Hiragana Kanji Romaji
End-user can express query entirely in hiragana!
Sec. 2.2.1
C. D Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008

54. Solutions (to Arabic), Technical
 Preprocessing
 Spelling checking (a whole new problem)
 Tatweel
 Repetitions
 Normalization
To improve matching (i.e. increase recall)
 Change ‫ء‬ ،‫آ‬ ،‫إ‬ ،‫أ‬ ،‫ا‬ to ‫ا‬
 Change ‫ة‬ to ‫ه‬
 Change ‫ى‬ to ‫ي‬
 Can use rule-based approaches to solve some common issues
 Dialects and MSA might need different approaches

55. Question Answering**
Hammo et al. QARAB: A Question Answering System to Support the Arabic Language.
Workshop on Computational Approaches to Semitic Languages. ACL 2002

56. Available Tools
 Mawdoo3’s AI tools (MALTK)
 Arabic Treebank
 Arabic WordNet
 MySQL database
 SUMO Ontology
 Java
 Dr. Mostafa Jarrar’s Arabic
Ontology
 Quran Corpus
 Dr. Nizar Habash’s MADAR
 Al-Ma’any dictionary
 Microsoft Arabic Toolkit (ATK)
 BabelNet
 Farasa

57. Solutions, Research
 Governments and private organizations must
 Support research
 i.e. pour millions!
 E.g. ‫االردني‬ ‫العهد‬ ‫ولي‬ ‫من‬ ‫ضاد‬ ‫مبادرة‬
 Find a way to standardize and centralize Arabic
 Teach Arabic professionally
 The Semantic web
 Understanding the context
‫مباد‬ ‫ضمن‬ ‫ستندرج‬ ‫التي‬ ‫المبادرة‬ ‫وتهدف‬‫رات‬
‫للغة‬ ‫سفراء‬ ‫إعداد‬ ‫إلى‬ ،‫العهد‬ ‫ولي‬ ‫مؤسسة‬
‫ف‬ ‫استخدامها‬ ‫تعزيز‬ ‫على‬ ‫يعملون‬ ‫العربية‬‫ي‬
‫ع‬ ‫وتعزيز‬ ‫وإنشاء‬ ،‫المعرفة‬ ‫ميادين‬ ‫مختلف‬‫دد‬
،‫العربية‬ ‫باللغة‬ ‫للتواصل‬ ‫ات‬ّ‫ص‬‫المن‬ ‫من‬
‫الحياة‬ ‫مجاالت‬ ‫جميع‬ ‫في‬ ‫واستخدامها‬
‫والتكنولوجية‬ ‫والعلمية‬ ‫العملية‬.

58. Summary
 NLU has many challenges and opportunities
 Arabic NLU has even more challenges and opportunities

59. Acknowledgment
 Dr. Abdallah Abo Shmais
 Linguistic Research Manager at Mawdoo3
 Dr. Abed Al-Hakim Freihat
 Senior Computational Linguist at Mawdoo3

60. ‫وغاي‬ ً‫ا‬‫لفظ‬ ‫هللا‬ ‫كتاب‬ ‫وسعت‬‫ة‬‫عن‬ ‫ضقت‬ ‫وما‬‫آي‬‫وعظات‬ ‫به‬
‫وصف‬ ‫عن‬ ‫اليوم‬ ‫أضيق‬ ‫فكيف‬‫آلة‬‫لمخترعات‬ ‫أسماء‬ ‫وتنسيق‬
‫كامن‬ ‫الدر‬ ‫أحشائه‬ ‫في‬ ‫البحر‬ ‫أنا‬‫عن‬ ‫الغواص‬ ‫سألوا‬ ‫فهل‬‫صدفات‬‫ي‬
‫محاس‬ ‫وتبلى‬ ‫أبلى‬ ‫ويحكم‬ ‫فيا‬‫ني‬‫أساتي‬ ‫الدواء‬ ‫عز‬ ‫وإن‬ ‫ومنكم‬
‫فإنني‬ ‫للزمان‬ ‫تكلوني‬ ‫فال‬‫أخاف‬‫عليكم‬‫وفاتي‬ ‫تحين‬ ‫أن‬
‫ابراهيم‬ ‫حافظ‬

61. Thank You