  As a fundamental tool for natural language processing (NLP), the
part-of-speech (POS) tagger assigns the POS label to each word in a sentence. A
novel lightweight POS tagger based on word embeddings is proposed and named
GWPT (green word-embedding-based POS tagger) in this work. Following the green
learning (GL) methodology, GWPT contains three modules in cascade: 1)
representation learning, 2) feature learning, and 3) decision learning modules.
The main novelty of GWPT lies in representation learning. It uses
non-contextual or contextual word embeddings, partitions embedding dimension
indices into low-, medium-, and high-frequency sets, and represents them with
different N-grams. It is shown by experimental results that GWPT offers
state-of-the-art accuracies with fewer model parameters and significantly lower
computational complexity in both training and inference as compared with
deep-learning-based methods.
