This document summarizes research on discourse annotation for Arabic. It discusses the Leeds Arabic Discourse Treebank, approaches to identifying discourse connectives and relations, and semantic and rhetorical methods for discourse segmentation. It also describes work developing a taxonomy of Arabic coherence relations. The key findings are that agreement on connective identification is high but relation assignment is lower, and systems for relation recognition and segmentation achieved accuracy over 80% using syntactic and semantic features.

1. Survey on Discourse
Annotation for Arabic
A. Algarni, H. Alharbi and N. Almutairy
Supervisor: Dr. A. Alsaif
April 23, 2013
Kingdom of Saudi Arabia
Ministry of Higher Education
Imam Mohammed Ibn Saud Islamic University
College of computer and Information Sciences
CS465 - Natural Language Processing –
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2. Outline
 Introduction
 The Leeds Arabic Discourse Treebank
 Discourse Connective Recognition
 Discourse Relation Recognition
 Semantic-Based Segmentation
 Discourse Segmentation Based on Rhetorical
Methods
 A Comprehensive Taxonomy of Arabic Discourse
Coherence Relations
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3. Introduction
 Linguistic annotation covers any descriptive
or analytic notations applied to raw language
data.
 Annotated Discourse Corpora can be very
useful to facilitate theoretical studies along
with contributing in the development of NLP
applications.
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4. Applications
 Information extraction
 Question-answering
 Summarization
 Machine translation, generation.
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5. Discourse Relations and
Discourse Connectives
 Discourse Relation is the way that two
arguments (text segments) logically connected.
 Temporal, Comparison, Causal, Expansion..etc
 Discourse Connective (DC) :A lexical marker
used to link two abstract objects in a text.
 Abstract Object (AO) : Abstract objects in
discourse are things like proposition
, events, facts and opinions.
 Argument (Arg) : A text expressing an abstract
object and linked by a DC.
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6. The Leeds Arabic Discourse
Treebank
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• First effort towards producing an Arabic
Discourse Treebank was introduced in 2011
by A. Alsaif and K. Markert.
• Collected a large set of Arabic discourse
connectives using text analysis and corpus
based techniques.
•Final list contains 107 discourse
connectives.

7. Types of Discourse connectives
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8. Types of Relations
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9. Types of Relations Cont..
 COMPARISON.Similarity:
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10. Arabic Discourse Annotation Tool
(ADA) and Annotation Process
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11. Annotation Methodology
1. Measuring whether annotators agree on
the binary decision on whether an item
constitutes a discourse connective in
context.
2. Measuring whether annotators agree on
which discourse relation an identified
connective expresses. As annotators can
use sets of relations for a connective.
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12. Results
 Agreement in task 1 is highly reliable
(N=23331) percentage agreement of
0.95, kappa of 0.88.
 Agreement in task 2 (relation assignment)
is relatively low (N=5586), percentage
agreement of 0.66, kappa 0.57, and alpha
of 0.58.
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13. Discourse Connective Recognition
 To distinguish between discourse and non-
discourse usage of a connective.
 Example: once, while.
 A. Alsaif and K.Markert (2011) introduced
a Connective identifier for Arabic based on
syntactic features.
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14. Discourse Connective Recognition
by A. Alsaif and K.Markert (2011)
Features:
 Surface Features (SConn)
 Lexical features of surrounding words
(Lex)
 Example
Arg1DC
Arg2.
[Children might be tired]Arg1 [and]DC [feel sleepy]Arg2 during school time if they did
not sleep well
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15. Features:
 Part of Speech features (POS)
 Syntactic category of related phrases
(Syn) (E.g.: / the school is
very large and beautiful)
 Al-Masdar feature.
Discourse Connective Recognition
by A. Alsaif and K.Markert (2011) Cont…
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16.  Results
Discourse Connective Recognition
by A. Alsaif and K.Markert (2011) Cont…
Features Acurr K
Baseline (not Conn) 68.9 0
M1 Conn only 75.7 0.48
Tokenization by white space + auto tagger
M2
M3
M4
Conn+ SConn+Lex
Conn+ SConn+Lex+POS
Conn+SConn+Lex+POS+Masdar
85.6 0.62
87.6 0.69
88.5 0.70
ATB-based features
M5
M6
M7
Conn+SConn+Lex
Conn+SConn+Lex+Syn/POS
Conn+SConn+Lex+Syn/POS+Masdar
86.2 0.65
91.2 0.79
92.4 0.82
M8
M9
Conn+SConn+Syn
SConn+Lex+Syn+Masdar
91.2 0.79
91.2 0.79
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17. Discourse Relation Recognition
 To identify the type of the relation
 A. Alsaif and K.Markert (2011) introduced
the first algorithms to automatically
identify relations for Arabic
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18. Features:
 Connective features
 Words and POS of arguments
 Masdar
 Tense and Negation
 Length, Distance and Order Features
 Argument Parent
 Production Rules
Discourse Relation Recognition
by A. Alsaif and K.Markert (2011)
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19. Results
Acurr kFeatures
All connectives (6039)
52.5 0Baseline (CONJUNCTION)
77.2 0.60
78.7 0.66
78.3 0.65
Conn only (1)
Conn+Conn f+ Arg f (37)
Conn+Conn f+ Arg f+ Production rules (1237)
M1
M2
M3
Excluding wa at BOP (3813)
35 0Baseline (CONJUNCTION)
74.3 0.65
77.0 0.69
76.7 0.69
Conn only (1)
Conn+Conn f+ Arg f (37)
Conn+Conn f+ Arg f+ Production rules (1237)
M1
M2
M3
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20. Results
Acurr kFeatures
All connectives (6039)
62.4 0Baseline (EXPANSION )
88.7 0.78
88.7 0.78
Conn only (1)
Conn+Conn f+ Arg f (37)
M1
M2
Excluding wa at BOP (3813)
41.8 0Baseline (EXPANSION)
82.7 0.74
83.5 0.75
Conn only (1)
Conn+Conn f+ Arg f (37)
M1
M2
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21. Semantic-Based Segmentation of
Arabic Texts
 Corpus Analysis
 Definition: Let L be a list of candidate
segments connectors, each element c in L is
classified based on its effects on the text
segmentation as either active or passive
 Examples:
.1[
][
[
.2]][
]
[
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22. Segmentation Process
 Identifying the connectors that indicate
complete segments.
 Locating the active connectors.
 Resolving the case where adjacent active
connectors exist.
 Setting the segments boundaries.
 Creating the final list of segments.
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23. Discussion
 evaluate the segmentation process, they
collected ten essays.
 Each essay ranges between 500 and 700
words.
 After implementing the segmentation
process.
 Gave the output to judges to evaluate
them in terms of two factors: correct
hit and incorrect hit.
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24. Discussion Cont..
Incorrect hitCorrect hitEssay
0331
1152
0253
1234
0205
1296
1267
2338
0269
02210
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25. Arabic Discourse Segmentation
Based on Rhetorical Methods
 This Method is depends on the meaning of
the connector " " in Arabic language.
 There are six types of " " classified into
two classes, "Fasl" and "Wasl " :
 "Fasl " : segmenting place.
 "Wasl " : unsegmenting but connecting
the text.
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26. Types of Connector " "
ClassExampleType
Fasl
Fasl
Fasl
Wasl
Wasl
Wasl
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27. The Arabic sentence
Segmentation System
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28. Feature Extraction
•The following are the features of " ":
X3 = noun and X7 = accusative mark.
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29. Experiment and Results
 They used 1200 instances for training.
 They used 293 instances for testing after
testing there are 290 correct and 3
incorrect instances.
 The result with:
94.68%Recall
96.82%Precision
98.98 %Accuracy
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30. A Comprehensive Taxonomy of Arabic
Discourse Coherence Relations
 Coherence relations are classified into two
types: explicit relations and implicit
relations.
exampleCoherence relations
I am very happy because I got
excellent marks in exams.
Explicit relations
I am very happy. I got excellent
marks in exams.
Implicit relations.
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31. The procedure of creating an Arabic
Taxonomy of Coherence Relations
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32. Examples of Implicit Arabic
relations
 "Impossible condition / " :
 "Cascaded questioning/ :
(
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33. Results
 They got a set of 47 Arabic coherence
relations.
coherence relations.Result
From English coherence
relations.
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additional Arabic explicit
coherence relations.
12
Arabic implicit relations.4
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34. Conclusion
Discourse Annotation is a very fertile field
and it has many NLP applications, for
Arabic there are some challenges due to
the lack of annotated corpora and studies.
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35. Thank You
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