Year: 2017
Type: conference-paper
Abstract: This paper focuses on comparing between using Support Vector Machine based ranking (SVM Rank ) and Bidirectional Long-Short-Term-Memory (bi-LSTM) neuralnetwork based sequence labeling in building a state-of-the-art Arabic part-ofspeech 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 Rankbased tagger yields to further improvements.We also show that gains realized using embeddings may not be additive with the gains achieved due to features.We are open-sourcing both the SVM Rank and the bi-LSTM based systems for the research community.
DOI: 10.18653/v1/w17-1316
Institution: Hamad bin Khalifa University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2740918723