  Deep neural networks employ multiple processing layers for learning text
representations to alleviate the burden of manual feature engineering in
Natural Language Processing (NLP). Such text representations are widely used to
extract features from unlabeled data. The word segmentation is a fundamental
and inevitable prerequisite for many languages. Sindhi is an under-resourced
language, whose segmentation is challenging as it exhibits space omission,
space insertion issues, and lacks the labeled corpus for segmentation. In this
paper, we investigate supervised Sindhi Word Segmentation (SWS) using unlabeled
data with a Subword Guided Neural Word Segmenter (SGNWS) for Sindhi. In order
to learn text representations, we incorporate subword representations to
recurrent neural architecture to capture word information at morphemic-level,
which takes advantage of Bidirectional Long-Short Term Memory (BiLSTM),
self-attention mechanism, and Conditional Random Field (CRF). Our proposed
SGNWS model achieves an F1 value of 98.51% without relying on feature
engineering. The empirical results demonstrate the benefits of the proposed
model over the existing Sindhi word segmenters.
