![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
14](https://image.slidesharecdn.com/discourseannotationforarabic2-130429085314-phpapp01/85/Discourse-annotation-for-arabic-2-14-320.jpg)
![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]][
]
[
21](https://image.slidesharecdn.com/discourseannotationforarabic2-130429085314-phpapp01/85/Discourse-annotation-for-arabic-2-21-320.jpg)
Discourse annotation for arabic 2
- 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 – 1
- 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 2
- 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. 3
- 4. Applications Information extraction Question-answering Summarization Machine translation, generation. 4
- 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. 5
- 6. The Leeds Arabic Discourse Treebank 6 • 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 7
- 9. Types of Relations Cont.. COMPARISON.Similarity: 9
- 10. Arabic Discourse Annotation Tool (ADA) and Annotation Process 10
- 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. 11
- 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. 12
- 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. 13
- 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 14
- 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… 15
- 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 16
- 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 17
- 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) 18
- 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 19
- 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 20
- 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]][ ] [ 21
- 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. 22
- 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. 23
- 24. Discussion Cont.. Incorrect hitCorrect hitEssay 0331 1152 0253 1234 0205 1296 1267 2338 0269 02210 24
- 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. 25
- 26. Types of Connector " " ClassExampleType Fasl Fasl Fasl Wasl Wasl Wasl 26
- 27. The Arabic sentence Segmentation System 27
- 28. Feature Extraction •The following are the features of " ": X3 = noun and X7 = accusative mark. 28
- 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 29
- 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. 30
- 31. The procedure of creating an Arabic Taxonomy of Coherence Relations 31
- 32. Examples of Implicit Arabic relations "Impossible condition / " : "Cascaded questioning/ : ( 32
- 33. Results They got a set of 47 Arabic coherence relations. coherence relations.Result From English coherence relations. 31 additional Arabic explicit coherence relations. 12 Arabic implicit relations.4 33
- 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. 34
- 35. Thank You 35