Arabic Flair + fastText Part-of-Speech tagging Model (Egyptian and Levant)

Pretrained Part-of-Speech tagging model built on a joint corpus written in Egyptian and Levantine (Jordanian, Lebanese, Palestinian, Syrian) dialects with code-switching of Egyptian Arabic and English. The model is trained using Flair (forward+backward)and fastText embeddings.

Pretraining Corpora:

This sequence labeling model was pretrained on three corpora jointly:

  1. 4 Dialects A Dialectal Arabic Datasets containing four dialects of Arabic, Egyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR). Each dataset consists of a set of 350 manually segmented and PoS tagged tweets.
  2. UD South Levantine Arabic MADAR A Dataset with 100 manually-annotated sentences taken from the MADAR (Multi-Arabic Dialect Applications and Resources) project by Shorouq Zahra.
  3. Parts of the Cairo Students Code-Switch (CSCS) corpus developed for "Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus" by Hamed et al.

Usage

from flair.data import Sentence
from flair.models import SequenceTagger
  
tagger = SequenceTagger.load("megantosh/flair-arabic-dialects-codeswitch-egy-lev")
sentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')
tagger.predict(sentence)
for entity in sentence.get_spans('pos'):
    print(entity)

Example

Citation

if you use this model in your work, please consider citing this work:

@unpublished{MMHU21
author = "M. Megahed",
title = "Sequence Labeling Architectures in Diglossia",
note = "In preparation",
}
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