This document summarizes work on building a lexical database for Modern Standard Arabic. It describes extracting entries from corpora and annotating them with morphological and syntactic details like inflection paradigms and subcategorization frames. Machine learning was used to automatically extend the database by predicting features for new words and extracting subcategorization frames from treebanks. A web application was also created for lexicography work. The work has produced several open-source Arabic language resources.

1. Lexical Profiling for Arabic
Mohammed Attia, Pavel Pecina, Antonio Toral, Lamia
Tounsi, Josef van Genabith
National Centre for Language Technology (NCLT),
School of Computing, Dublin City University
Funded by:
Enterprise Ireland, the Irish Research Council for Science
Engineering and Technology (IRCSET), and
the EU projects PANACEA and META-NET

2. Overview
• Introduction
• Building the lexical database for Arabic
– Corpus-based Selection of Entries
– Morphological Details: Inflectional Paradigms
– Syntactic Details: Subcategorization Frames
• Web Application
• Conclusion

3. Introduction
• Modern Standard Arabic vs. Classical
Arabic
• Current State of Arabic Lexicography
– Lexicons are not corpus-based
– Buckwalter Electronic Dictionary and Arabic
Morphological Analyser
– No lexica for subcategorization frames
• Importance of Lexical Resources

4. Introduction
• Arabic Morphotactics

5. Aim
• Constructing a lexical database of Modern
Standard Arabic
• Constructing a database for Arabic
subcategorization frames

6. Methodology
Lexical Details
• Using a medium-scale manually created lexicon of
10,799 lemmas
• Using statistics from a 1 billion word corpus (annotated
by MADA)
– 90% from the LDC's Arabic Gigaword
– 10% collected from the Al-Jazeera website
Subcategorization Details
• Using a medium-scale manually created lexicon of 2,901
lemma-frame types
• Using the Penn Arabic Treebank of 22,524 sentences,
and 587,665 words

7. Extending the Lexical Database
• Start-off with a seed lexicon
– Three Lexical Databases, manually constructed
• 5,925 nominal lemmas, with details on:
– Gender and number
– Inflection paradigm (13 continuation classes)
– Humanness
• 1,529 verb lemmas, with details on:
– Transitivity
– Whether passive is allowed or not
– Whether the imperative is allowed or not
• 490 patterns (456 for nominals and 34 for verbs)
• lemma-root look up database

8. Methodology

9. Extending the Lexical Database
• Automatically Extending the Lexical
Database: Lexical Enrichment
– Data-driven filtering technique
• 40,648 lemmas (in Buckwalter or SAMA 3.1)
• Statistics from three web search engines
• Statistics from the corpus annotated by MADA
• 29,627 lemmas (left after filtering)

10. Extending the Lexical Database
Automatically Extending the Lexical
Database: Feature Enrichment
– Machine Learning
– Multilayer Peceptron classification algorithm
– Training Data: 4,816 nominals and 1,448 verbs
– Classes for nominals: continuation classes (or inflection
paths), the semantico-grammatical feature of humanness,
and POS (noun or adjective)
– Classes for verbs: transitivity, allowing the passive voice,
and allowing the imperative mood
– We feed these datasets with frequency statistics from the
corpus and build a vector grid.

11. Extending the Lexical Database
• Extending the Lexical Database
– Feature enrichment using Machine Learning

12. Extending the Lexical Database
• Extending the Lexical Database
– With Machine Learning we add:

18,000 new lemmas:

12,974 nominals

5,034 verbs

13. Extending the Lexical Database
• Handling Broken Plurals
jAnib (side)
jawAnib (sides)
Poor handling of broken plural in Buckwalter
(4) <lemmaID>jAnib_1</lemmaID> <voc>jAnib</voc>
<pos>jAnib/NOUN</pos> <gloss>side/aspect</gloss>
(5) <lemmaID>jAnib_1</lemmaID> <voc>jawAnib</voc>
<pos>jawAnib/NOUN</pos> <gloss>sides/aspects</gloss>
Two differences: voc and gloss

14. Extending the Lexical Database
• Extracting Broken Plurals
<gloss>side/aspect</gloss>
<gloss>sides/aspects</gloss>

We use Levenshtein Distance which measures the difference
between two strings (here glosses having the same lemmaID).
distance of 2 / length of the first string = 0.15
(within the threshold 0.4)

We collect 2,266 candidates

15. Extending the Lexical Database
• Validating Broken Plurals
<voc>jAnib</voc> singular
pattern is: fAEil
regex is: .A.i.
<voc>jawAnib</voc> plural
pattern is: fawAEil
regex is: .awA.i.
Pattern database: 135 singular patterns that choose from a
set of 82 broken plural patterns
2,266 candidates -> 1,965 are validated (87%)

16. Extending the Lexical Database
• Interesting statistics on Arabic plurals
Insights from the corpus:
5,570 lemmas have a feminine plural suffix
1,942 lemmas have a masculine plural suffix
2,730 lemmas with a broken plural forms

17. Extraction of Subcat Frames
• Importance of subcategorization frames
• Advantage of Automatic Extraction
• Available Resource on Arabic Subcat
Frames:
– none except Arabic LFG Parser (Attia,
2008) - available as open source

18. Extraction of Subcat Frames
What are LFG subcat frames?

Governable GFs (SUBJ, OBJ, OBJϴ, OBLϴ, COMP
and XCOMP)

Non-governable GFs (ADJ and XADJ)
π<gf1,gf2,…gfn>
{iEotamada Al-Tifolu EalaY wAlidati-hi
“The child relied on his mother”
{iEotamada<(↑SUBJ)( ↑OBL>alaY)>

19. Extraction of Subcat Frames
Automatic extraction of subcat frames

The ATB contains 22,524 sentences

LFG Annotation algorithm (DCU)

Traversing trees and looking for dependencies.

Lemmatization

We extract

7,746 lemma-frame types (for verbs, nouns and
adjectives)

20. Extraction of Subcat Frames
Estimating the Subcategorization Probability

21. Extraction of Subcat Frames
Evaluation the Subcategorization Extraction

22. Extraction of Subcat Frames
Evaluation the Subcategorization Extraction

23. Web Application
• AraComLex Lexicon Writing Application
www.cngl.ie/aracomlex

24. Byproducts of the Work
A number of open-source Resources:
• finite-state morphological transducer

Arabic morphological patterns

Subcategorization frames

Arabic lemma frequency counts

25. Conclusion
• We successfully use machine learning to predict
morpho-syntactic features for newly acquired
words.
• We successfully extract subcategorization
frames from the Penn Arabic Treebank
• We build specifications and implementation for
an Arabic lexicographic web application.