1. Computational Investigation of Palestinian Arabic Dialects
Ezra Daya
Rafi Talmon
Shuly Wintner

2. Background Fieldwork study refers to Arabic
dialects spoken by people in 250 localities –
Northern and central parts of Israel.
Localities in the West Bank.
Southern Lebanese communities in Galilee.
1948’s Palestinian refugees in existing Arabic localities .

3. Background cont. Colloquial Arabic features:
Non-official spoken language, usually not written.
Differs from place to place.
The similarity/distance between the Arabic dialects
can be measured
Considered by the speakers as less prestigious
compared to the official Arabic.

4. Background cont. Work performed by special teams :
Collecting and processing fieldwork material such as recorded interviews and linguistic questionnaires.
Transcription of the material that constitutes the basis of our work.
Defining an accurate description of the language varieties of Palestinian colloquial Arabic, their characteristics, and their geographical distribution.

5. Transcribed Text Sample

6. Objectives using computational linguistic techniques in order to:
Publication of the vast collected material
using computational linguistic techniques
in order to:
Create lexicons and glossaries for Arabic
dialects automatically.
Create a linguistic atlas to graphically measure the similarities among the dialects.
Better understanding of morphological and phonemic dialectology features.

7. Linguistic Atlas

8. The challenge – Rich Morphology
Semitic languages such as Arabic, have a rich
morphology and contain highly inflected forms.
Example:
axdat is 3nd, singular, feminine, past form of the verb axad
Obtained by concatenating the suffix ‘at’ and reducing
the vowel ‘a’ to the base axad.

9. Rich Morphology cont. Arabic has a complex system of morphology based
on triconsonantal roots that is common in Semitic
languages.
For example, there are 10 verb patterns, each
of which can be inflected in 3 numbers, 2 genders,
3 persons, several tenses and aspects, and can be
suffixed by several pronominal forms.

10. Traditional Approach Disadvantages:
Assignment of linguists performing grammatical analysis of the transcribed texts and manually creating lexicon, glossaries and linguistic atlas.
Disadvantages:
Lack of sophistication.
Time consuming.
Expensive human resources.

11. Innovative Approach Devise an automated analysis of these
transcribed texts, in order to obtain:
An automated creation of a glossary to organize all the lexical items by grammatical features. i.e. root, pattern etc.
Isolation of the phonetic and morphological features and characteristic of specific dialects in this surveyed area.
Measurement of dialect similarity.
Automated processing provides accuracy
and efficiency .

12. Linguistic Technologies
For this research we intend to exploit
existing computational linguistics technology for
the investigation of Palestinian Arabic dialects
by using:
Finite-State technology.
Machine learning techniques.
Computational dialectology.

13. Finite State Technology
Employing the Xerox finite state tools and
techniques which are:
Useful and efficient programs that process text in natural languages.
Concentrating on morphological analysis and generation.
Giving access to finite state operations and a regular expression compiler.

14. Machine Learning
Machine learning is concerned with the question of how to construct computer programs that
automatically improve with experience.
Two distinguished learning frameworks
according to the amount of supervision used:
Supervised learning
when the learning algorithm is presented with pairs of strings of symbols., i.e. inflected and uninflected forms.
Unsupervised learning
when the algorithm is presented merely with a single set of words, and must work out what the morphological relationships are.

15. Computational Dialectology
Use measures to compute the distance
between two given dialects and to define
geographical dialect boundaries.
Example: Edit Distance
The distance could be set sensitive to
phonological similarities.
Example:

16. Previous Related Work
Morphological Tagging of the Qur’an:
The system facilitates a variety of queries on the Qur’anic text that make reference to the words and their linguistic attributes and provides full morphological tagging of its words.
The core of the system is a set of finite-state based
rules which describe the morpho-phonological and
morpho-syntactic phenomena of the Qur’anic
language.
The system is currently being used for teaching and research purposes.