  We propose a new type of representation learning method that models words,
phrases and sentences seamlessly. Our method does not depend on word
segmentation and any human-annotated resources (e.g., word dictionaries), yet
it is very effective for noisy corpora written in unsegmented languages such as
Chinese and Japanese. The main idea of our method is to ignore word boundaries
completely (i.e., segmentation-free), and construct representations for all
character $n$-grams in a raw corpus with embeddings of compositional
sub-$n$-grams. Although the idea is simple, our experiments on various
benchmarks and real-world datasets show the efficacy of our proposal.
