Arabic POS Tagging: Don’t Abandon Feature Engineering Just Yet

Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki


Abstract
This paper focuses on comparing between using Support Vector Machine based ranking (SVM-Rank) and Bidirectional Long-Short-Term-Memory (bi-LSTM) neural-network based sequence labeling in building a state-of-the-art Arabic part-of-speech tagging system. Using SVM-Rank leads to state-of-the-art results, but with a fair amount of feature engineering. Using bi-LSTM, particularly when combined with word embeddings, may lead to competitive POS-tagging results by automatically deducing latent linguistic features. However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements. We also show that gains that realized by using embeddings may not be additive with the gains achieved by the features. We are open-sourcing both the SVM-Rank and the bi-LSTM based systems for free.
Anthology ID:
W17-1316
Volume:
Proceedings of the Third Arabic Natural Language Processing Workshop
Month:
April
Year:
2017
Address:
Valencia, Spain
Venues:
WANLP | WS
SIG:
SEMITIC
Publisher:
Association for Computational Linguistics
Note:
Pages:
130–137
Language:
URL:
https://aclanthology.org/W17-1316
DOI:
10.18653/v1/W17-1316
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W17-1316.pdf